Paper Citation Record · LEDGER

Reinforcement Learning for LLM Post-Training: A Survey

As of 5 August 2026, this Paper Citation Record lists 96 of 96 outbound references and 35 inbound Pith citation observations for arXiv:2407.16216.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2407.16216 v4

Coverage vector

measured 96 of 96 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-23T22:35:35.287039Z

measured 131 of 131 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 35 of 35 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:39:08.244079Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

96 of 96 outbound references displayed

verified exact6
verified fuzzy81
unresolved8
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

7
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 41ee95c0-ed31-4565-8582-4bec1964d30a · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

Reinforcement Learning for LLM Post-Training: A Survey Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.479044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5ab86491-1f11-4173-8582-3f0e9a83c074 · outbound

This paper cites an unresolved cited work.

Reinforcement Learning for LLM Post-Training: A Survey Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-05-23T22:35:51.482409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d6f3456d-b78f-48fb-91fe-f27f3bdc1e3b · outbound

This paper cites Training a helpful and harmless assistant with reinforcement learning from human feedback.

Reinforcement Learning for LLM Post-Training: A Survey Training a helpful and harmless assistant with reinforcement learning from human feedback

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.486346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8bdeb67b-9621-411b-ac3d-f21b74ef9217 · outbound

This paper cites an unresolved cited work.

Reinforcement Learning for LLM Post-Training: A Survey Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-05-23T22:35:51.416492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a7510842-3eba-4869-9e23-ef019229ee74 · outbound

This paper cites The claude 3 model family: Opus, sonnet, haiku.

Reinforcement Learning for LLM Post-Training: A Survey The claude 3 model family: Opus, sonnet, haiku

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.475329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 710a6eb1-3f02-4ac1-b880-ee18e4d8020b · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Reinforcement Learning for LLM Post-Training: A Survey Gemini: A Family of Highly Capable Multimodal Models

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-05-23T22:35:50.304630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation df9518d0-8156-496d-a784-1b0110a74a29 · outbound

This paper cites Rlhf workflow: From reward modeling to online rlhf.

Reinforcement Learning for LLM Post-Training: A Survey Rlhf workflow: From reward modeling to online rlhf

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.378072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 19cb9fd8-1e69-4193-8b94-52e65e2b02ce · outbound

This paper cites Iterative preference learning from human feedback: Bridging theory and practice for rlhf under kl-constraint.

Reinforcement Learning for LLM Post-Training: A Survey Iterative preference learning from human feedback: Bridging theory and practice for rlhf under kl-constraint

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.397901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a5bedc3d-3223-4c19-9b81-8134f8cca61a · outbound

This paper cites Bowman, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, and Jared Kaplan.

Reinforcement Learning for LLM Post-Training: A Survey Bowman, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, and Jared Kaplan

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.639341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 737657c7-ad21-433e-883a-e52a6140e94e · outbound

This paper cites Rlaif: Scaling reinforcement learning from human feedback with ai feedback.

Reinforcement Learning for LLM Post-Training: A Survey Rlaif: Scaling reinforcement learning from human feedback with ai feedback

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.573067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5bad53b0-48e8-417a-b203-af6f6f171569 · outbound

This paper cites an unresolved cited work.

Reinforcement Learning for LLM Post-Training: A Survey Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-05-23T22:35:51.583731Z

Source-reported events for the cited work

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Observation f9169425-faf9-488b-a6c6-bb694d11a966 · outbound

This paper cites Manning, and Chelsea Finn.

Reinforcement Learning for LLM Post-Training: A Survey Manning, and Chelsea Finn

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.545251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 90bfbf4d-0a77-4b8f-ac5e-338a80697b01 · outbound

This paper cites Smaug: Fixing failure modes of preference optimisation with dpo-positive.

Reinforcement Learning for LLM Post-Training: A Survey Smaug: Fixing failure modes of preference optimisation with dpo-positive

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.539904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 12833e43-3931-4d83-814e-a1eb31bcf212 · outbound

This paper cites β-dpo: Direct preference optimization with dynamic β.

Reinforcement Learning for LLM Post-Training: A Survey β-dpo: Direct preference optimization with dynamic β

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.549126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 74acbde2-9219-46fb-bbbd-3f62361eb642 · outbound

This paper cites A general theoretical paradigm to understand learning from human preferences.

Reinforcement Learning for LLM Post-Training: A Survey A general theoretical paradigm to understand learning from human preferences

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.565905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ee29a6ea-2640-41c1-bd86-8919971c360d · outbound

This paper cites sdpo: Don’t use your data all at once.

Reinforcement Learning for LLM Post-Training: A Survey sdpo: Don’t use your data all at once

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.615981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f2cd99c0-c23d-4eae-bca1-fdd7cb9a4f4f · outbound

This paper cites From r to q∗: Your language model is secretly a q-function.

Reinforcement Learning for LLM Post-Training: A Survey From r to q∗: Your language model is secretly a q-function

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.693376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 37068e4e-f7d9-4b17-8516-1620c2cff80c · outbound

This paper cites Token-level direct preference optimization.

Reinforcement Learning for LLM Post-Training: A Survey Token-level direct preference optimization

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.493725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2e864275-507d-4ae1-9998-22abf3fcbc60 · outbound

This paper cites Self-rewarding language models.

Reinforcement Learning for LLM Post-Training: A Survey Self-rewarding language models

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.470156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5ab8bc47-8da8-4d3e-99f2-450e8633bd52 · outbound

This paper cites Some things are more cringe than others: Iterative preference optimization with the pairwise cringe loss.

Reinforcement Learning for LLM Post-Training: A Survey Some things are more cringe than others: Iterative preference optimization with the pairwise cringe loss

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.498202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 14e27ea6-a84f-445e-8675-73cda460eed1 · outbound

This paper cites Kto: Model alignment as prospect theoretic optimization.

Reinforcement Learning for LLM Post-Training: A Survey Kto: Model alignment as prospect theoretic optimization

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.531924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation eb2c37f0-9d2b-40a5-bde0-424cc1cc69a9 · outbound

This paper cites Offline regularised reinforcement learning for large language models alignment.

Reinforcement Learning for LLM Post-Training: A Survey Offline regularised reinforcement learning for large language models alignment

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.514400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2dc7bb51-2f68-439e-bc2a-7eec04505a71 · outbound

This paper cites Orpo: Monolithic preference optimization without reference model.

Reinforcement Learning for LLM Post-Training: A Survey Orpo: Monolithic preference optimization without reference model

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.507139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3a15b19e-901b-4da0-895f-1ff4d2d85cad · outbound

This paper cites Paft: A parallel training paradigm for effective llm fine-tuning.

Reinforcement Learning for LLM Post-Training: A Survey Paft: A parallel training paradigm for effective llm fine-tuning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.462277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2c05bf88-20f3-4fd2-854a-c08d4b8f7cc1 · outbound

This paper cites Disentangling length from quality in direct preference optimization.

Reinforcement Learning for LLM Post-Training: A Survey Disentangling length from quality in direct preference optimization

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.489672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation cba4f34d-36e6-4e27-8479-93be185c267e · outbound

This paper cites Simpo: Simple preference optimization with a reference-free reward.

Reinforcement Learning for LLM Post-Training: A Survey Simpo: Simple preference optimization with a reference-free reward

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.524829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d7983a5b-7419-4b44-949b-68ce7060fac5 · outbound

This paper cites Back to basics: Revisiting reinforce style optimization for learning from human feedback in llms.

Reinforcement Learning for LLM Post-Training: A Survey Back to basics: Revisiting reinforce style optimization for learning from human feedback in llms

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.441352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ff98a8ae-3b4e-4d9f-aaf1-44c676d78743 · outbound

This paper cites Liu, and Xuanhui Wang.

Reinforcement Learning for LLM Post-Training: A Survey Liu, and Xuanhui Wang

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.559480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d4d7fde4-792c-4b0d-b6a0-3cddc5a58f94 · outbound

This paper cites Rrhf: Rank responses to align language models with human feedback without tears.

Reinforcement Learning for LLM Post-Training: A Survey Rrhf: Rank responses to align language models with human feedback without tears

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.580163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 11a31772-b1e4-4f5d-b2c1-99ac4f709805 · outbound

This paper cites Preference ranking optimization for human alignment.

Reinforcement Learning for LLM Post-Training: A Survey Preference ranking optimization for human alignment

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.503370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e0b4a298-5c89-452c-b199-8df7ba70b1a6 · outbound

This paper cites Negating negatives: Alignment without human positive samples via distributional dispreference optimization.

Reinforcement Learning for LLM Post-Training: A Survey Negating negatives: Alignment without human positive samples via distributional dispreference optimization

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.419438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e143ba21-90f9-4991-871d-9f73a25da5c9 · outbound

This paper cites Negative preference optimization: From catastrophic collapse to effective unlearning.

Reinforcement Learning for LLM Post-Training: A Survey Negative preference optimization: From catastrophic collapse to effective unlearning

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.511504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation aea6477c-9704-42a0-b7ed-bd91fc5afd24 · outbound

This paper cites Contrastive preference optimization: Pushing the boundaries of llm performance in machine translation.

Reinforcement Learning for LLM Post-Training: A Survey Contrastive preference optimization: Pushing the boundaries of llm performance in machine translation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.536251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 81382c5c-effc-4216-bb54-8f9c4e55463e · outbound

This paper cites Mankowitz, Doina Precup, and Bilal Piot.

Reinforcement Learning for LLM Post-Training: A Survey Mankowitz, Doina Precup, and Bilal Piot

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.510618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a4f2e5ab-84a8-4f7b-8232-be7600e3cb30 · outbound

This paper cites A minimaximalist approach to reinforcement learning from human feedback.

Reinforcement Learning for LLM Post-Training: A Survey A minimaximalist approach to reinforcement learning from human feedback

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.415463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d32da5e4-3c98-43c7-ba72-639be4d84958 · outbound

This paper cites Direct nash optimization: Teaching language models to self-improve with general preferences.

Reinforcement Learning for LLM Post-Training: A Survey Direct nash optimization: Teaching language models to self-improve with general preferences

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.401392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a8b485f9-bed1-48bb-b1a1-db9f3f5814d1 · outbound

This paper cites Beyond reverse kl: Generalizing direct preference optimization with diverse divergence constraints.

Reinforcement Learning for LLM Post-Training: A Survey Beyond reverse kl: Generalizing direct preference optimization with diverse divergence constraints

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.408714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7456c3d9-499d-4871-9292-2ecdbe5a8a2b · outbound

This paper cites an unresolved cited work.

Reinforcement Learning for LLM Post-Training: A Survey Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-05-23T22:35:51.391681Z

Source-reported events for the cited work

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Observation a57386fa-1f0c-4055-976e-6be0bc487193 · outbound

This paper cites A markovian decision process.

Reinforcement Learning for LLM Post-Training: A Survey A markovian decision process

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.388440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8ab25870-c9a6-46db-82e5-00fd6a328bc9 · outbound

This paper cites Hashimoto.

Reinforcement Learning for LLM Post-Training: A Survey Hashimoto

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.394727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a1778653-468c-4c57-ae85-77d0e4da3496 · outbound

This paper cites Bleu: a method for automatic evaluation of machine translation.

Reinforcement Learning for LLM Post-Training: A Survey Bleu: a method for automatic evaluation of machine translation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.412117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 32f7872b-193f-4e3c-b8f5-2b2cb02ff5ce · outbound

This paper cites Rouge: A package for automatic evaluation of summaries.

Reinforcement Learning for LLM Post-Training: A Survey Rouge: A package for automatic evaluation of summaries

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.482168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7a2547ee-ce4c-427e-bdf1-793bd3cf4150 · outbound

This paper cites Weinberger, and Yoav Artzi.

Reinforcement Learning for LLM Post-Training: A Survey Weinberger, and Yoav Artzi

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.661237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 83c152fe-d6fe-4aa6-8797-04e68279cce9 · outbound

This paper cites an unresolved cited work.

Reinforcement Learning for LLM Post-Training: A Survey Unresolved cited work

Reference 44

Resolution: unresolved; raw_fallback, observed 2026-05-23T22:35:51.429877Z

Source-reported events for the cited work

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Observation 696cf556-b69d-47cd-905b-9a70a9bd247f · outbound

This paper cites Truthfulqa: Measuring how models mimic human falsehoods.

Reinforcement Learning for LLM Post-Training: A Survey Truthfulqa: Measuring how models mimic human falsehoods

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.442972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4d46e208-6801-4766-a3ec-de799503b78d · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Reinforcement Learning for LLM Post-Training: A Survey Chain-of-thought prompting elicits reasoning in large language models

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.446993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 611436f5-ae08-47ce-84a4-aa10272c94d3 · outbound

This paper cites Ziegler, Ryan Lowe, Chelsea V oss, Alec Radford, Dario Amodei, and Paul Christiano.

Reinforcement Learning for LLM Post-Training: A Survey Ziegler, Ryan Lowe, Chelsea V oss, Alec Radford, Dario Amodei, and Paul Christiano

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.665586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b89e7f9e-2706-4d0b-b0f6-dac876fbcc1f · outbound

This paper cites Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, Eric Chu, Jonathan H.

Reinforcement Learning for LLM Post-Training: A Survey Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, Eric Chu, Jonathan H

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.576240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 48144c6b-97a2-42f1-a558-51791ce80eea · outbound

This paper cites High-Dimensional Continuous Control Using Generalized Advantage Estimation.

Reinforcement Learning for LLM Post-Training: A Survey High-Dimensional Continuous Control Using Generalized Advantage Estimation

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-05-23T22:35:50.309976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6e5b5ed8-83a9-4526-bca2-2fd5350c0a3b · outbound

This paper cites Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J.

Reinforcement Learning for LLM Post-Training: A Survey Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.444955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 985f1914-c302-4e1d-b75d-366da530af38 · outbound

This paper cites Liu, and Jialu Liu.

Reinforcement Learning for LLM Post-Training: A Survey Liu, and Jialu Liu

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.696964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ebebe5be-521a-4bce-bf94-a49137173aa8 · outbound

This paper cites Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study.

Reinforcement Learning for LLM Post-Training: A Survey Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:35:50.282923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 914132a8-46b6-498b-93ee-1e777a015ab2 · outbound

This paper cites Maas, Raymond E.

Reinforcement Learning for LLM Post-Training: A Survey Maas, Raymond E

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.689798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 44b9fd79-d89e-4ca1-88e4-5c4a81c67f8f · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Reinforcement Learning for LLM Post-Training: A Survey Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-05-23T22:35:50.294514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c127a630-88c7-4e30-9455-117dd01e7e21 · outbound

This paper cites Hellaswag: Can a machine really finish your sentence? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.

Reinforcement Learning for LLM Post-Training: A Survey Hellaswag: Can a machine really finish your sentence? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.712970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6220a988-6323-437a-a3a2-8994ac19879a · outbound

This paper cites Kwok, Zhenguo Li, Adrian Weller, and Weiyang Liu.

Reinforcement Learning for LLM Post-Training: A Survey Kwok, Zhenguo Li, Adrian Weller, and Weiyang Liu

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.652034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 93e86d94-962d-42d4-91ad-ad6bb5319b60 · outbound

This paper cites Xing, Hao Zhang, Joseph E.

Reinforcement Learning for LLM Post-Training: A Survey Xing, Hao Zhang, Joseph E

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.671441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 45c694d0-ef68-4c86-b975-89963006acf4 · outbound

This paper cites Pythia: A suite for analyzing large language models across training and scaling.

Reinforcement Learning for LLM Post-Training: A Survey Pythia: A suite for analyzing large language models across training and scaling

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.679386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation af334596-b044-46de-a42a-9ed0357a83bd · outbound

This paper cites Solar 10.7b: Scaling large language models with simple yet effective depth up-scaling.

Reinforcement Learning for LLM Post-Training: A Survey Solar 10.7b: Scaling large language models with simple yet effective depth up-scaling

Reference 60

Resolution: malformed identifier; raw_fallback, observed 2026-05-23T22:35:51.619674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7dffecc0-3606-4704-8e5d-56b776ea295d · outbound

This paper cites Orca: Progressive learning from complex explanation traces of gpt-4.

Reinforcement Learning for LLM Post-Training: A Survey Orca: Progressive learning from complex explanation traces of gpt-4

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.605131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation cbbc87f2-12e0-40f3-81a2-9b5a366cc81a · outbound

This paper cites Ultrafeedback: Boosting language models with high-quality feedback.

Reinforcement Learning for LLM Post-Training: A Survey Ultrafeedback: Boosting language models with high-quality feedback

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.590154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6cac6db5-fd88-47cc-8280-b276c08dc9aa · outbound

This paper cites Measuring massive multitask language understanding.

Reinforcement Learning for LLM Post-Training: A Survey Measuring massive multitask language understanding

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.593896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e9d142e5-78ac-4ab3-845a-ec23f0747172 · outbound

This paper cites Winogrande: An adversarial winograd schema challenge at scale.

Reinforcement Learning for LLM Post-Training: A Survey Winogrande: An adversarial winograd schema challenge at scale

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.609644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation bf26ad90-ca31-454c-9f01-ca8cd4f79f12 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Reinforcement Learning for LLM Post-Training: A Survey Training Verifiers to Solve Math Word Problems

Reference 65

Resolution: verified exact; local_arxiv, observed 2026-05-23T22:35:50.288719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 737de695-d107-44c9-89d1-eac482791dbf · outbound

This paper cites Generalized preference optimization: A unified approach to offline alignment.

Reinforcement Learning for LLM Post-Training: A Survey Generalized preference optimization: A unified approach to offline alignment

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.570012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b3ea4c40-b1e4-47f9-9a82-ea6c2e86583e · outbound

This paper cites Language models are unsupervised multitask learners.

Reinforcement Learning for LLM Post-Training: A Survey Language models are unsupervised multitask learners

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.580449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7bb92faf-99cb-49ac-a7d1-4fb093f39379 · outbound

This paper cites Llama 2: Open foundation and fine-tuned chat models.

Reinforcement Learning for LLM Post-Training: A Survey Llama 2: Open foundation and fine-tuned chat models

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.587175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 829acdd5-5433-4a4f-bc7c-8e798de4b516 · outbound

This paper cites The cringe loss: Learning what language not to model.

Reinforcement Learning for LLM Post-Training: A Survey The cringe loss: Learning what language not to model

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.630359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d2973944-a7d3-47e5-adff-5e0316f35255 · outbound

This paper cites Hashimoto.

Reinforcement Learning for LLM Post-Training: A Survey Hashimoto

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.529270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 07ea3b54-dd38-4bb9-ab20-65e19538d55a · outbound

This paper cites Advances in prospect theory: Cumulative representation of uncertainty.

Reinforcement Learning for LLM Post-Training: A Survey Advances in prospect theory: Cumulative representation of uncertainty

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.526071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 66fabb02-424f-437d-9bf7-87525f861d18 · outbound

This paper cites Proximal policy optimization algorithms.

Reinforcement Learning for LLM Post-Training: A Survey Proximal policy optimization algorithms

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.532606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 577b2d2d-1ae5-46ed-8d3f-1497d9cb6209 · outbound

This paper cites an unresolved cited work.

Reinforcement Learning for LLM Post-Training: A Survey Unresolved cited work

Reference 73

Resolution: unresolved; raw_fallback, observed 2026-05-23T22:35:51.633995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 07bff1ea-da89-472d-ad0b-613b07be387b · outbound

This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

Reinforcement Learning for LLM Post-Training: A Survey Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 74

Resolution: verified exact; local_arxiv, observed 2026-05-23T22:35:50.299816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1c6b788f-266b-496c-97aa-6397074e733c · outbound

This paper cites Phi-2: The surprising power of small language models.

Reinforcement Learning for LLM Post-Training: A Survey Phi-2: The surprising power of small language models

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.563796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f7ef9e94-119e-42f1-a00e-468bdeafab0f · outbound

This paper cites an unresolved cited work.

Reinforcement Learning for LLM Post-Training: A Survey Unresolved cited work

Reference 76

Resolution: unresolved; raw_fallback, observed 2026-05-23T22:35:51.514946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation daeb35c3-6af9-41c4-9bfc-1681de57d7b2 · outbound

This paper cites Instruction-following evaluation for large language models.

Reinforcement Learning for LLM Post-Training: A Survey Instruction-following evaluation for large language models

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.518764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a63075f8-1aa7-4c43-a0a0-a6a0848b2539 · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

Reinforcement Learning for LLM Post-Training: A Survey Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.545512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 17579027-2b4a-43c1-bcc2-7dcb0935f694 · outbound

This paper cites Gonzalez, and Ion Stoica.

Reinforcement Learning for LLM Post-Training: A Survey Gonzalez, and Ion Stoica

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.518033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3df9b7fd-5e9e-4321-a88c-f726d6c706ac · outbound

This paper cites Simple statistical gradient-following algorithms for connectionist reinforcement learning.

Reinforcement Learning for LLM Post-Training: A Survey Simple statistical gradient-following algorithms for connectionist reinforcement learning

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.683292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0d944cac-3c8d-46d5-a075-87e31d2ec18c · outbound

This paper cites Buy 4 REINFORCE samples, get a baseline for free!.

Reinforcement Learning for LLM Post-Training: A Survey Buy 4 REINFORCE samples, get a baseline for free!

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.552696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2db9056c-7483-4753-8b60-19302329c864 · outbound

This paper cites Learning to rank for information retrieval.

Reinforcement Learning for LLM Post-Training: A Survey Learning to rank for information retrieval

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.553126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5d0e78e9-0a98-4bc1-8f8f-ccdf8b8776a1 · outbound

This paper cites an unresolved cited work.

Reinforcement Learning for LLM Post-Training: A Survey Unresolved cited work

Reference 83

Resolution: unresolved; raw_fallback, observed 2026-05-23T22:35:51.642809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b00bca99-5099-4c51-b2f5-c087e9a0f940 · outbound

This paper cites Openassistant conversations – democratizing large language model alignment.

Reinforcement Learning for LLM Post-Training: A Survey Openassistant conversations – democratizing large language model alignment

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.528599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation caa32368-772c-4fac-aa0e-8d37c2064eb6 · outbound

This paper cites Hashimoto.

Reinforcement Learning for LLM Post-Training: A Survey Hashimoto

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.559788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f41a71fb-eece-4c83-bb8b-db9a8deef6e5 · outbound

This paper cites Secrets of rlhf in large language models part ii: Reward modeling.

Reinforcement Learning for LLM Post-Training: A Survey Secrets of rlhf in large language models part ii: Reward modeling

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.571733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 732b60fd-bad0-4dd5-b241-e2d2243d738d · outbound

This paper cites Safe rlhf: Safe reinforcement learning from human feedback.

Reinforcement Learning for LLM Post-Training: A Survey Safe rlhf: Safe reinforcement learning from human feedback

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.657844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a3466e86-ac7c-4a7d-a803-3cfa805308bf · outbound

This paper cites Lipton, and J.

Reinforcement Learning for LLM Post-Training: A Survey Lipton, and J

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.648279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9ead4ea7-08ef-4c83-b1c6-b5622e5e4d4a · outbound

This paper cites A paradigm shift in machine translation: Boosting translation performance of large language models.

Reinforcement Learning for LLM Post-Training: A Survey A paradigm shift in machine translation: Boosting translation performance of large language models

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.701061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 29ad9da9-968d-42cf-934d-f2077af82f7a · outbound

This paper cites On the limitations of the elo, real-world games are transitive, not additive.

Reinforcement Learning for LLM Post-Training: A Survey On the limitations of the elo, real-world games are transitive, not additive

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.705266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 35116a69-8d42-4971-b104-f0274d074a9c · outbound

This paper cites Self-play preference optimization for language model alignment.

Reinforcement Learning for LLM Post-Training: A Survey Self-play preference optimization for language model alignment

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.577157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e6375735-596d-49b2-b62b-63f99fa40c54 · outbound

This paper cites Schapire.

Reinforcement Learning for LLM Post-Training: A Survey Schapire

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.622936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e2eb508d-fac0-49e5-9c80-046ac23e41ea · outbound

This paper cites Llm-blender: Ensembling large language models with pairwise ranking and generative fusion.

Reinforcement Learning for LLM Post-Training: A Survey Llm-blender: Ensembling large language models with pairwise ranking and generative fusion

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.716609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a58d8c4c-8b7f-4c89-8fbe-e47710ed9aa2 · outbound

This paper cites Orca 2: Teaching small language models how to reason.

Reinforcement Learning for LLM Post-Training: A Survey Orca 2: Teaching small language models how to reason

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.521463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 10e9967c-47a8-4a89-a04e-dd069ca98ca6 · outbound

This paper cites On decoding strategies for neural text generators.

Reinforcement Learning for LLM Post-Training: A Survey On decoding strategies for neural text generators

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.501985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6892be44-c36d-4e92-b3c9-77c487ee1612 · outbound

This paper cites Insights into alignment: Evaluating dpo and its variants across multiple tasks.

Reinforcement Learning for LLM Post-Training: A Survey Insights into alignment: Evaluating dpo and its variants across multiple tasks

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.522881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 422cf210-88ee-448d-9e2c-acbc1f761e69 · outbound

This paper cites Is dpo superior to ppo for llm alignment? a comprehensive study.

Reinforcement Learning for LLM Post-Training: A Survey Is dpo superior to ppo for llm alignment? a comprehensive study

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T22:35:51.563056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Pith citing papers

Observation b90d553c-2bc3-4b50-a060-c07269eedf3a · inbound

ReGA: Model-Based Safeguard for LLMs via Representation-Guided Abstraction cites this paper.

ReGA: Model-Based Safeguard for LLMs via Representation-Guided Abstraction Reinforcement Learning for LLM Post-Training: A Survey

Reference 41

Resolution: metadata mismatch; local_arxiv, observed 2026-05-19T11:37:16.014516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4bb4783a-b6b6-4dad-bb60-51766d8a175a · inbound

Exploring the Secondary Risks of Large Language Models cites this paper.

Exploring the Secondary Risks of Large Language Models Reinforcement Learning for LLM Post-Training: A Survey

Reference 47

Resolution: metadata mismatch; local_arxiv, observed 2026-05-19T09:42:13.985529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ebd692bf-faeb-40d8-9d2e-c6ff0bde5d3a · inbound

EyeMulator: Improving Code Language Models by Mimicking Human Visual Attention cites this paper.

EyeMulator: Improving Code Language Models by Mimicking Human Visual Attention Reinforcement Learning for LLM Post-Training: A Survey

Reference 65

Resolution: verified exact; local_arxiv, observed 2026-05-18T20:56:51.816772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5f5c0311-173e-4dcc-b796-221408854dff · inbound

Token Buncher: Shielding LLMs from Harmful Reinforcement Learning Fine-Tuning cites this paper.

Token Buncher: Shielding LLMs from Harmful Reinforcement Learning Fine-Tuning Reinforcement Learning for LLM Post-Training: A Survey

Reference 52

Resolution: metadata mismatch; local_arxiv, observed 2026-05-18T20:41:50.436344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation fd962b78-169e-4c63-8873-44cb2333d49d · inbound

The Landscape of Agentic Reinforcement Learning for LLMs: A Survey cites this paper.

The Landscape of Agentic Reinforcement Learning for LLMs: A Survey Reinforcement Learning for LLM Post-Training: A Survey

Reference 14

Resolution: metadata mismatch; local_arxiv, observed 2026-05-18T19:21:48.376249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation cf9f655d-a984-48d5-a5e7-b7d55157347a · inbound

A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models cites this paper.

A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models Reinforcement Learning for LLM Post-Training: A Survey

Reference 273

Resolution: unresolved; no resolver link, observed 2026-08-05T10:39:08.244079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6b7f581-ab53-4d4e-a416-17ebd7aa179b · inbound

Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial cites this paper.

Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial Reinforcement Learning for LLM Post-Training: A Survey

Reference 265

Resolution: unresolved; no resolver link, observed 2026-08-05T04:50:32.523428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a15aba6-4d84-491b-bcb5-292e9565c697 · inbound

EPT Benchmark: Evaluation of Persian Trustworthiness in Large Language Models cites this paper.

EPT Benchmark: Evaluation of Persian Trustworthiness in Large Language Models Reinforcement Learning for LLM Post-Training: A Survey

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T23:03:16.345319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df2adc50-b14f-4bd0-b9ea-a23274a516e8 · inbound

Query Expansion in the Age of Pre-trained and Large Language Models: A Comprehensive Survey cites this paper.

Query Expansion in the Age of Pre-trained and Large Language Models: A Comprehensive Survey Reinforcement Learning for LLM Post-Training: A Survey

Reference 110

Resolution: verified exact; local_arxiv, observed 2026-05-18T18:11:42.974325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f6300611-2eb1-4a5e-a01d-40ed2a7fe2ec · inbound

Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle cites this paper.

Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle Reinforcement Learning for LLM Post-Training: A Survey

Reference 178

Resolution: unresolved; no resolver link, observed 2026-08-04T16:07:43.117616Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b16c4e7-1b7c-4756-b793-777bda1359ce · inbound

SCOPE-RL: Stable and Quantitative Control of Policy Entropy in RL Post-Training cites this paper.

SCOPE-RL: Stable and Quantitative Control of Policy Entropy in RL Post-Training Reinforcement Learning for LLM Post-Training: A Survey

Reference 19

Resolution: metadata mismatch; local_arxiv, observed 2026-05-21T20:30:35.472865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1bfb6bdd-e510-45ea-b57f-d08bcbf58d1a · inbound

Voting with the Graph: Stable RLAIF via Topological Consistency Maximization cites this paper.

Voting with the Graph: Stable RLAIF via Topological Consistency Maximization Reinforcement Learning for LLM Post-Training: A Survey

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T09:28:13.001684Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 206d97fe-d9ff-4a62-b3a1-b31282853cbe · inbound

Maximizing the efficiency of human feedback in AI alignment: a comparative analysis cites this paper.

Maximizing the efficiency of human feedback in AI alignment: a comparative analysis Reinforcement Learning for LLM Post-Training: A Survey

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T22:02:16.789125Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee8de5cb-22a3-44e6-a3c6-92adee7be1f8 · inbound

RACC: Representation-Aware Coverage Criteria for LLM Safety Testing cites this paper.

RACC: Representation-Aware Coverage Criteria for LLM Safety Testing Reinforcement Learning for LLM Post-Training: A Survey

Reference 51

Resolution: metadata mismatch; local_arxiv, observed 2026-05-16T08:17:36.555589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 57d1d6d9-8bba-4db0-b197-cc43eed7a2ad · inbound

Safety Alignment as Continual Learning: Mitigating the Alignment Tax via Orthogonal Gradient Projection cites this paper.

Safety Alignment as Continual Learning: Mitigating the Alignment Tax via Orthogonal Gradient Projection Reinforcement Learning for LLM Post-Training: A Survey

Reference 22

Resolution: metadata mismatch; local_arxiv, observed 2026-05-16T06:40:41.730258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 92febfba-be07-4bce-956e-4459d2f6ea71 · inbound

VC-Soup: Value-Consistency Guided Multi-Value Alignment for Large Language Models cites this paper.

VC-Soup: Value-Consistency Guided Multi-Value Alignment for Large Language Models Reinforcement Learning for LLM Post-Training: A Survey

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-05-15T09:39:54.359255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a87cf072-c53d-482d-9bf8-bd7207b29052 · inbound

The Art of (Mis)alignment: How Fine-Tuning Methods Effectively Misalign and Realign LLMs in Post-Training cites this paper.

The Art of (Mis)alignment: How Fine-Tuning Methods Effectively Misalign and Realign LLMs in Post-Training Reinforcement Learning for LLM Post-Training: A Survey

Reference 61

Resolution: metadata mismatch; local_arxiv, observed 2026-05-11T00:45:50.664592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b479ae6a-3f9f-4648-b20e-7e70fce63411 · inbound

Mobile GUI Agent Privacy Personalization with Trajectory Induced Preference Optimization cites this paper.

Mobile GUI Agent Privacy Personalization with Trajectory Induced Preference Optimization Reinforcement Learning for LLM Post-Training: A Survey

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-05-11T09:11:01.113709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 07619aec-be4c-422f-9420-92393b1334c8 · inbound

ProjLens: Unveiling the Role of Projectors in Multimodal Model Safety cites this paper.

ProjLens: Unveiling the Role of Projectors in Multimodal Model Safety Reinforcement Learning for LLM Post-Training: A Survey

Reference 37

Resolution: metadata mismatch; local_arxiv, observed 2026-05-11T12:46:05.553273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ad446046-50cc-4b8d-aaff-a5c5998ef8af · inbound

Pref-CTRL: Preference Driven LLM Alignment using Representation Editing cites this paper.

Pref-CTRL: Preference Driven LLM Alignment using Representation Editing Reinforcement Learning for LLM Post-Training: A Survey

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-05-11T21:11:19.342574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b5582d48-55c7-485b-9d8f-d0866a6e76b0 · inbound

Generating Place-Based Compromises Between Two Points of View cites this paper.

Generating Place-Based Compromises Between Two Points of View Reinforcement Learning for LLM Post-Training: A Survey

Reference 73

Resolution: verified exact; local_arxiv, observed 2026-05-11T22:01:12.088276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 70210b30-9bb9-4993-a0ae-25a3c5d1a2e2 · inbound

Rethinking Agentic Reinforcement Learning In Large Language Models cites this paper.

Rethinking Agentic Reinforcement Learning In Large Language Models Reinforcement Learning for LLM Post-Training: A Survey

Reference 95

Resolution: verified exact; local_arxiv, observed 2026-05-12T10:21:29.964715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7d51e331-406f-4c13-9633-1e0b2752d58f · inbound

Rethinking Agentic Reinforcement Learning In Large Language Models cites this paper.

Rethinking Agentic Reinforcement Learning In Large Language Models Reinforcement Learning for LLM Post-Training: A Survey

Reference 95

Resolution: verified exact; local_arxiv, observed 2026-05-11T22:11:16.721393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9e58f133-85e0-40f4-b5a1-a9d462a264cd · inbound

Rethinking Agentic Reinforcement Learning In Large Language Models cites this paper.

Rethinking Agentic Reinforcement Learning In Large Language Models Reinforcement Learning for LLM Post-Training: A Survey

Reference 95

Resolution: verified exact; local_arxiv, observed 2026-05-19T17:02:40.688757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8cb754df-3e9a-48c1-9039-97b4dcbdc9ff · inbound

Stable-GFlowNet: Toward Diverse and Robust LLM Red-Teaming via Contrastive Trajectory Balance cites this paper.

Stable-GFlowNet: Toward Diverse and Robust LLM Red-Teaming via Contrastive Trajectory Balance Reinforcement Learning for LLM Post-Training: A Survey

Reference 9

Resolution: metadata mismatch; local_arxiv, observed 2026-05-11T15:41:17.804578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2045f3d6-b30b-407c-adac-2243f11ef719 · inbound

Agents Should Replace Narrow Predictive AI as the Orchestrator in 6G AI-RAN cites this paper.

Agents Should Replace Narrow Predictive AI as the Orchestrator in 6G AI-RAN Reinforcement Learning for LLM Post-Training: A Survey

Reference 45

Resolution: metadata mismatch; local_arxiv, observed 2026-05-13T02:17:06.980568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ebaa50f6-de7e-4137-8a96-7d14b955712b · inbound

UNIPO: Unified Interactive Visual Explanation for RL Fine-Tuning Policy Optimization cites this paper.

UNIPO: Unified Interactive Visual Explanation for RL Fine-Tuning Policy Optimization Reinforcement Learning for LLM Post-Training: A Survey

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-05-13T01:42:03.971815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a5d940f6-bc0f-45d8-9b48-614becafa94a · inbound

Distinguishable Deletion: Unifying Knowledge Erasure and Refusal for Large Language Model Unlearning cites this paper.

Distinguishable Deletion: Unifying Knowledge Erasure and Refusal for Large Language Model Unlearning Reinforcement Learning for LLM Post-Training: A Survey

Reference 73

Resolution: metadata mismatch; local_arxiv, observed 2026-05-19T21:52:48.235735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 514183ef-2b4a-4335-a865-9861bc6f4aa6 · inbound

ReCrit: Transition-Aware Reinforcement Learning for Scientific Critic Reasoning cites this paper.

ReCrit: Transition-Aware Reinforcement Learning for Scientific Critic Reasoning Reinforcement Learning for LLM Post-Training: A Survey

Reference 37

Resolution: metadata mismatch; local_arxiv, observed 2026-05-20T22:53:49.325337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1970e252-73e9-4025-abff-429e5b4c6713 · inbound

DOG-DPO:Dynamic Optimization in Geometry for Safety Alignment cites this paper.

DOG-DPO:Dynamic Optimization in Geometry for Safety Alignment Reinforcement Learning for LLM Post-Training: A Survey

Reference 8

Resolution: metadata mismatch; local_arxiv, observed 2026-07-02T12:06:56.031254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f6851d24-0f0d-4dbb-9124-28228106628e · inbound

Reward-Free Code Alignment from Pretrained or Fine-Tuned LLM: Unpacking the Trade-offs for Code Generation cites this paper.

Reward-Free Code Alignment from Pretrained or Fine-Tuned LLM: Unpacking the Trade-offs for Code Generation Reinforcement Learning for LLM Post-Training: A Survey

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-06-30T08:34:26.678705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a7acfd67-0f89-4f61-9688-2203b6f2ddcd · inbound

Training Therapeutic Judges and Multi-Agent Systems for Human-Aligned Mental Health Support cites this paper.

Training Therapeutic Judges and Multi-Agent Systems for Human-Aligned Mental Health Support Reinforcement Learning for LLM Post-Training: A Survey

Reference 95

Resolution: verified exact; local_arxiv, observed 2026-07-01T12:35:43.844499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8da3d815-7164-449f-9afb-35f55a83bc06 · inbound

Meta-Learning Preferences for Multilingual LLM Alignment cites this paper.

Meta-Learning Preferences for Multilingual LLM Alignment Reinforcement Learning for LLM Post-Training: A Survey

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-02T05:36:16.410995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59a1edaa-924c-4831-a5be-ff2c11c0bace · inbound

Sound Probabilistic Safety Bounds for Large Language Models cites this paper.

Sound Probabilistic Safety Bounds for Large Language Models Reinforcement Learning for LLM Post-Training: A Survey

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-01T10:22:41.813908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6cadd64-ae02-4da7-8972-71698e65181d · inbound

On-Policy Distillation for LLM Safety: A Routing Approach to Template-Robust Realignment cites this paper.

On-Policy Distillation for LLM Safety: A Routing Approach to Template-Robust Realignment Reinforcement Learning for LLM Post-Training: A Survey

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-30T11:44:36.074744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.