Paper Citation Record · LEDGER

Explicit Preference Optimization: No Need for an Implicit Reward Model

As of 7 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2506.07492.

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

pith.paper-citation-record.v1
2506.07492 v1

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:40:18.045861Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T16:11:49.149334Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T09:11:01.276341Z

Reference resolution

57 of 57 outbound references displayed

verified exact0
verified fuzzy12
unresolved45
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 814c293d-8351-4c84-a266-071dd259de96 · outbound

This paper cites GPT-4 Technical Report.

Explicit Preference Optimization: No Need for an Implicit Reward Model GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.805167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7733375b-fae1-45c0-94d4-4195aa3d2497 · outbound

This paper cites Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs.

Explicit Preference Optimization: No Need for an Implicit Reward Model Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.810011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28a73e90-003e-4e7b-b6da-7f358d3c3f51 · outbound

This paper cites Llama 3 model card.

Explicit Preference Optimization: No Need for an Implicit Reward Model Llama 3 model card

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.814798Z

Source-reported events for the cited work

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Observation 587bad29-1ff8-4a50-9e75-684abf84c47f · outbound

This paper cites Direct Preference Optimization with an Offset.

Explicit Preference Optimization: No Need for an Implicit Reward Model Direct Preference Optimization with an Offset

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.819727Z

Source-reported events for the cited work

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Observation 79c6ad72-af3f-4126-a1aa-0601e6c05547 · outbound

This paper cites G., Guo, Z.

Explicit Preference Optimization: No Need for an Implicit Reward Model G., Guo, Z

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.824269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c395d6de-c621-4c82-81ed-498728b3de65 · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Explicit Preference Optimization: No Need for an Implicit Reward Model Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.828666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 946f90a8-9e91-4dea-b308-9bf73f94ebfa · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

Explicit Preference Optimization: No Need for an Implicit Reward Model Constitutional AI: Harmlessness from AI Feedback

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.833973Z

Source-reported events for the cited work

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Observation 99bb216e-2624-4f7b-be33-c84082939d0b · outbound

This paper cites G., Bradley, H., O’Brien, K., Hallahan, E., Khan, M.

Explicit Preference Optimization: No Need for an Implicit Reward Model G., Bradley, H., O’Brien, K., Hallahan, E., Khan, M

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.839067Z

Source-reported events for the cited work

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Observation c0ef421c-5e0e-41ef-a713-4ec9ce79430c · outbound

This paper cites and Rinaldo, A.

Explicit Preference Optimization: No Need for an Implicit Reward Model and Rinaldo, A

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:18.973562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a615d5e9-8a6f-43da-84cd-91beb628846a · outbound

This paper cites an unresolved cited work.

Explicit Preference Optimization: No Need for an Implicit Reward Model Unresolved cited work

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.848212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0320c2d-dee5-4d5f-b887-0426e3b56aea · outbound

This paper cites T., Li, Y., Lundberg, S., Nori, H., Palangi, H., Ribeiro, M.

Explicit Preference Optimization: No Need for an Implicit Reward Model T., Li, Y., Lundberg, S., Nori, H., Palangi, H., Ribeiro, M

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:18.946737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f4011480-7ab6-4093-8a94-ac2d90a9758d · outbound

This paper cites A survey on evaluation of large language models.

Explicit Preference Optimization: No Need for an Implicit Reward Model A survey on evaluation of large language models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:18.932346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 48ce16f0-6567-4693-b4f1-353b408a368f · outbound

This paper cites Bootstrapping Language Models with DPO Implicit Rewards.

Explicit Preference Optimization: No Need for an Implicit Reward Model Bootstrapping Language Models with DPO Implicit Rewards

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.861184Z

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Observation 7bcc5977-6d0d-40de-9a89-38f77c56e04e · outbound

This paper cites Ultrafeedback: Boosting language models with scaled ai feedback.

Explicit Preference Optimization: No Need for an Implicit Reward Model Ultrafeedback: Boosting language models with scaled ai feedback

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:18.916344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 23d7df53-4660-4f6d-8841-aae19e1168d8 · outbound

This paper cites Enhancing chat language models by scaling high-quality instructional conversations.

Explicit Preference Optimization: No Need for an Implicit Reward Model Enhancing chat language models by scaling high-quality instructional conversations

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.869779Z

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Observation b3d3c575-fa88-4367-a5b8-4c98693dcc9f · outbound

This paper cites Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators.

Explicit Preference Optimization: No Need for an Implicit Reward Model Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.873960Z

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Observation c7b3bf60-134f-42c1-a96a-de45a050d49f · outbound

This paper cites KTO: Model Alignment as Prospect Theoretic Optimization.

Explicit Preference Optimization: No Need for an Implicit Reward Model KTO: Model Alignment as Prospect Theoretic Optimization

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.878001Z

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Observation 29c4a37d-1f34-468f-90e3-68cb338f922a · outbound

This paper cites Towards Analyzing and Understanding the Limitations of DPO: A Theoretical Perspective.

Explicit Preference Optimization: No Need for an Implicit Reward Model Towards Analyzing and Understanding the Limitations of DPO: A Theoretical Perspective

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.882471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ac79af2-8955-4e80-8e72-19b881aba6d1 · outbound

This paper cites Bias and Fairness in Large Language Models: A Survey.

Explicit Preference Optimization: No Need for an Implicit Reward Model Bias and Fairness in Large Language Models: A Survey

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.886568Z

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Observation ee9c6377-7ddb-4e9b-8ca8-df6af4d58ad9 · outbound

This paper cites Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned.

Explicit Preference Optimization: No Need for an Implicit Reward Model Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.890649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9bf454c0-d0c9-470b-99ac-b9b245bc4f18 · outbound

This paper cites Learn Your Reference Model for Real Good Alignment.

Explicit Preference Optimization: No Need for an Implicit Reward Model Learn Your Reference Model for Real Good Alignment

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.894671Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 186c1af7-8365-4919-945b-3665b82d0bcd · outbound

This paper cites and Pierskalla, W.

Explicit Preference Optimization: No Need for an Implicit Reward Model and Pierskalla, W

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:18.890064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 69ecbe00-bcf7-4428-91ab-5fbd69ed5fa4 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Explicit Preference Optimization: No Need for an Implicit Reward Model DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.902738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e897ace9-7697-498a-ba60-dd00acfaa9c8 · outbound

This paper cites ORPO: Monolithic Preference Optimization without Reference Model.

Explicit Preference Optimization: No Need for an Implicit Reward Model ORPO: Monolithic Preference Optimization without Reference Model

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.906760Z

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Observation e1a6ffa6-1c87-438c-9b8e-5c3b6a11a067 · outbound

This paper cites Understanding the Learning Dynamics of Alignment with Human Feedback.

Explicit Preference Optimization: No Need for an Implicit Reward Model Understanding the Learning Dynamics of Alignment with Human Feedback

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.910923Z

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Observation d6a10d37-3832-4f5e-b2e4-bf3e7bf18d1e · outbound

This paper cites https://github.com/huggingface/trl/pull/1265.

Explicit Preference Optimization: No Need for an Implicit Reward Model https://github.com/huggingface/trl/pull/1265

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:18.875095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 65a14ff0-d858-4efd-8bd0-57f9600c6629 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Explicit Preference Optimization: No Need for an Implicit Reward Model Adam: A Method for Stochastic Optimization

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.920012Z

Source-reported events for the cited work

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Observation 8dba99e6-d37a-40a8-8f2b-710645e6e6eb · outbound

This paper cites Common learning constraints alter interpretations of direct preference optimization.

Explicit Preference Optimization: No Need for an Implicit Reward Model Common learning constraints alter interpretations of direct preference optimization

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:18.860673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 06e98cfe-841e-411d-b3be-83c479524cec · outbound

This paper cites H., Gonzalez, J.

Explicit Preference Optimization: No Need for an Implicit Reward Model H., Gonzalez, J

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.928495Z

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Observation 9daa881e-8c3b-4878-b8ce-b212ac68cc8f · outbound

This paper cites an unresolved cited work.

Explicit Preference Optimization: No Need for an Implicit Reward Model Unresolved cited work

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.933007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7c6d022-71b5-42c7-90d9-c7b5bcbd23f7 · outbound

This paper cites Policy Optimization in RLHF: The Impact of Out-of-preference Data.

Explicit Preference Optimization: No Need for an Implicit Reward Model Policy Optimization in RLHF: The Impact of Out-of-preference Data

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.937230Z

Source-reported events for the cited work

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Observation 08f0ace7-0ead-474d-aaaf-3a7fb6b1e2fe · outbound

This paper cites On the Limited Generalization Capability of the Implicit Reward Model Induced by Direct Preference Optimization.

Explicit Preference Optimization: No Need for an Implicit Reward Model On the Limited Generalization Capability of the Implicit Reward Model Induced by Direct Preference Optimization

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.941137Z

Source-reported events for the cited work

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Observation 9735fcff-1b7a-4138-b7d9-0ee2d8104416 · outbound

This paper cites L., Daly, R.

Explicit Preference Optimization: No Need for an Implicit Reward Model L., Daly, R

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:18.825067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3eaad74c-b477-4f61-ad2d-2514ce7af7ab · outbound

This paper cites SimPO: Simple Preference Optimization with a Reference-Free Reward.

Explicit Preference Optimization: No Need for an Implicit Reward Model SimPO: Simple Preference Optimization with a Reference-Free Reward

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.949816Z

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Observation a4d29e7f-976f-40d7-9278-28a683cbe2cb · outbound

This paper cites Active Preference Learning for Large Language Models.

Explicit Preference Optimization: No Need for an Implicit Reward Model Active Preference Learning for Large Language Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.953985Z

Source-reported events for the cited work

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Observation 31a19b43-e3f6-46ee-8746-81b6e9241408 · outbound

This paper cites Training language models to follow instructions with human feedback.

Explicit Preference Optimization: No Need for an Implicit Reward Model Training language models to follow instructions with human feedback

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.958243Z

Source-reported events for the cited work

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Observation 59fc338f-5672-41ee-81f7-61e83c74674c · outbound

This paper cites Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive.

Explicit Preference Optimization: No Need for an Implicit Reward Model Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.962472Z

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Observation 2444c89a-c34f-44c2-a429-af7a17a9eb79 · outbound

This paper cites Disentangling Length from Quality in Direct Preference Optimization.

Explicit Preference Optimization: No Need for an Implicit Reward Model Disentangling Length from Quality in Direct Preference Optimization

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.966403Z

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Observation 491e4e82-0774-4f99-87e1-63e182a5feaa · outbound

This paper cites Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning.

Explicit Preference Optimization: No Need for an Implicit Reward Model Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.970581Z

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Observation 8aa11bc4-da3e-4fd0-9bad-4b0f8063e226 · outbound

This paper cites and Schaal, S.

Explicit Preference Optimization: No Need for an Implicit Reward Model and Schaal, S

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:18.799960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1b783b59-d2c2-4763-b8b5-40991e1dd7b1 · outbound

This paper cites D., Ermon, S., and Finn, C.

Explicit Preference Optimization: No Need for an Implicit Reward Model D., Ermon, S., and Finn, C

Reference 41

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Observation e4315a4d-6138-4a5e-a77a-56c8f4f8748a · outbound

This paper cites Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization.

Explicit Preference Optimization: No Need for an Implicit Reward Model Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.982800Z

Source-reported events for the cited work

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Observation d7a20d07-a754-48c3-9370-5e3d676483f5 · outbound

This paper cites an unresolved cited work.

Explicit Preference Optimization: No Need for an Implicit Reward Model Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:40:18.774723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 62dbb63e-f8b3-434a-8ba4-510035797112 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Explicit Preference Optimization: No Need for an Implicit Reward Model Proximal Policy Optimization Algorithms

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.991410Z

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Observation 9f0c31c3-9de9-486f-a220-7e3369074278 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Explicit Preference Optimization: No Need for an Implicit Reward Model DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 45

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Source-reported events for the cited work

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Observation 1e8ed1e2-e9a0-46cb-861c-81de59126bae · outbound

This paper cites The importance of online data: U nderstanding preference fine-tuning via coverage.

Explicit Preference Optimization: No Need for an Implicit Reward Model The importance of online data: U nderstanding preference fine-tuning via coverage

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:18.760164Z

Source-reported events for the cited work

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Observation bf476f93-313a-4550-9249-1b0ca218ec18 · outbound

This paper cites M., Lowe, R., Voss, C., Radford, A., Amodei, D., and Christiano, P.

Explicit Preference Optimization: No Need for an Implicit Reward Model M., Lowe, R., Voss, C., Radford, A., Amodei, D., and Christiano, P

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:18.744990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ab772d2f-269f-40f3-bd2f-4188f6b385f7 · outbound

This paper cites S., and Bagnell, J.

Explicit Preference Optimization: No Need for an Implicit Reward Model S., and Bagnell, J

Reference 48

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Source-reported events for the cited work

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Observation c06bc6f8-2862-4405-a46a-971305cb837d · outbound

This paper cites Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data.

Explicit Preference Optimization: No Need for an Implicit Reward Model Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:18.011912Z

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Observation 6bf9cade-a35f-49df-802a-f4f1d1c79197 · outbound

This paper cites Generalized Preference Optimization: A Unified Approach to Offline Alignment.

Explicit Preference Optimization: No Need for an Implicit Reward Model Generalized Preference Optimization: A Unified Approach to Offline Alignment

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:18.016343Z

Source-reported events for the cited work

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Observation 1b95adbf-7431-45c7-ada4-c04e01e142f6 · outbound

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

Explicit Preference Optimization: No Need for an Implicit Reward Model Beyond reverse KL : Generalizing direct preference optimization with diverse divergence constraints

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:40:18.729750Z

Source-reported events for the cited work

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Observation 88349501-5533-413c-8a10-33c1c19c004b · outbound

This paper cites mDPO: Conditional Preference Optimization for Multimodal Large Language Models.

Explicit Preference Optimization: No Need for an Implicit Reward Model mDPO: Conditional Preference Optimization for Multimodal Large Language Models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:18.024969Z

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Observation a7cab4d7-a511-4c0c-860c-3f7a5215f708 · outbound

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

Explicit Preference Optimization: No Need for an Implicit Reward Model Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:18.028816Z

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Observation 3613ae8d-bd79-4c5b-9de9-0ce589cb6c87 · outbound

This paper cites A Survey of Large Language Models.

Explicit Preference Optimization: No Need for an Implicit Reward Model A Survey of Large Language Models

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:18.033007Z

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Unavailable: canonical work link unavailable.

Observation 96a4e02e-511b-44ed-9d04-e4057629f929 · outbound

This paper cites SLiC-HF: Sequence Likelihood Calibration with Human Feedback.

Explicit Preference Optimization: No Need for an Implicit Reward Model SLiC-HF: Sequence Likelihood Calibration with Human Feedback

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:18.036861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05aafb46-278e-4a2c-b53c-2bf326be5b86 · outbound

This paper cites Fine-Tuning Language Models from Human Preferences.

Explicit Preference Optimization: No Need for an Implicit Reward Model Fine-Tuning Language Models from Human Preferences

Reference 56

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Observation bc3da6ce-ceed-4082-a2d3-b4792f7fe476 · outbound

This paper cites write newline.

Explicit Preference Optimization: No Need for an Implicit Reward Model write newline

Reference 57

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Pith citing papers

Observation 8bb23c37-e426-491f-85e8-4e5cf80e523b · inbound

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

Mobile GUI Agent Privacy Personalization with Trajectory Induced Preference Optimization Explicit Preference Optimization: No Need for an Implicit Reward Model

Reference 7

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