Paper Citation Record · LEDGER

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning

As of 7 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2605.20256.

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

pith.paper-citation-record.v1
2605.20256 v3

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-30T18:38:09.609972Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

verified exact18
verified fuzzy14
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8158e572-a45c-4f6b-98fe-b5f5991614b9 · outbound

This paper cites Minif2f: a cross-system benchmark for formal olympiad-level mathemat- ics.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Minif2f: a cross-system benchmark for formal olympiad-level mathemat- ics

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T03:44:29.906125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 46bbace4-1160-4249-9603-213313f6a2a4 · outbound

This paper cites TravelPlanner: A Benchmark for Real-World Planning with Language Agents.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning TravelPlanner: A Benchmark for Real-World Planning with Language Agents

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-01T14:55:48.262269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation dbbc1452-4ab8-4b96-abff-b80eb66459a7 · outbound

This paper cites Learning to Reason under Off-Policy Guidance.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Learning to Reason under Off-Policy Guidance

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-07-01T14:55:48.256309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 59e770ff-1a96-4679-a5d8-cafb82a1ea4c · outbound

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

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-07-01T14:55:48.283447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a6151876-ba61-428d-b574-e0e569814d44 · outbound

This paper cites Proximal Policy Optimization Algorithms.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-07-01T14:55:48.276567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation fba08b0a-187b-4564-95fb-8c87d673f41c · outbound

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

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Training language models to follow instructions with human feedback

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T03:44:29.908097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2d246294-27db-41e4-8094-dcd04e7434af · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Constitutional AI: Harmlessness from AI Feedback

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-07-01T14:55:48.279183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2d5e0a45-f96f-456e-aaea-1d57fd339105 · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Direct preference optimization: Your language model is secretly a reward model

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T03:44:29.910177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 111c27f2-ad05-4fdc-ab6a-c28212bd77aa · outbound

This paper cites Self-refine: Iterative refinement with self-feedback.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Self-refine: Iterative refinement with self-feedback

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T03:44:29.904279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 31389433-791c-4249-9512-441e860fe66f · outbound

This paper cites Reflexion: Language agents with verbal reinforcement learning.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Reflexion: Language agents with verbal reinforcement learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T03:44:29.900407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 75573ef9-c1df-4fa6-9239-8102996716bc · outbound

This paper cites Training language models to self-correct via reinforcement learning.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Training language models to self-correct via reinforcement learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T03:44:29.902508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a88a53a4-6b40-4b3d-999b-3550d06a8dd9 · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-07-01T14:55:48.287734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 468692c0-e142-4be7-8418-8c0a3f8749da · outbound

This paper cites Let's Verify Step by Step.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Let's Verify Step by Step

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-07-01T14:55:48.262531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 053351d8-a1f8-4de5-8a8b-9de39a0419ff · outbound

This paper cites Large Language Models Cannot Self-Correct Reasoning Yet.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Large Language Models Cannot Self-Correct Reasoning Yet

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-07-01T14:55:48.273946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8e62c7fa-3064-4aa4-ba16-018c874f5038 · outbound

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

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-07-01T14:55:48.259700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b0dfe489-aae0-40a6-804c-cd4b4559b40f · outbound

This paper cites Math-shepherd: Verify and rein- force llms step-by-step without human annotations.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Math-shepherd: Verify and rein- force llms step-by-step without human annotations

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T03:44:29.898384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e90d92ab-85e5-4b7e-915a-3c958f7e8585 · outbound

This paper cites Critic: Large language models can self-correct with tool-interactive critiquing.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Critic: Large language models can self-correct with tool-interactive critiquing

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T03:44:29.896197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3cc9602d-eacd-45e8-80d9-d632ab112720 · outbound

This paper cites Generating sequences by learning to self-correct.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Generating sequences by learning to self-correct

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T03:44:29.892292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5c49ffdd-5c4a-433c-a872-1b3b56046084 · outbound

This paper cites Self-critiquing models for assisting human evaluators.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Self-critiquing models for assisting human evaluators

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-07-01T14:55:48.280312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 62cfb1a4-fa64-47c8-be10-747a8cd75bf1 · outbound

This paper cites Self-rewarding language models.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Self-rewarding language models

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T03:44:29.883887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2187babb-5a66-4b0e-9bc3-f61a0dd94ef3 · outbound

This paper cites Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T03:44:29.890515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 05bf0cfb-0094-4d53-977e-58f0dab65e35 · outbound

This paper cites ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-07-01T14:55:48.267554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0b52c1f3-20d6-414c-a0f4-58cce9c5c2f5 · outbound

This paper cites Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-07-01T14:55:48.277325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d49d42a1-cc67-4d85-ab7d-241102b51bb8 · outbound

This paper cites The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-07-01T14:55:48.264942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b90a97b5-82a6-4100-81b2-cd3ab7c3b356 · outbound

This paper cites Reinforcement Learning for Reasoning in Large Language Models with One Training Example.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-07-01T14:55:48.284952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ca022e7d-b208-48d7-a81b-30f6cfedad54 · outbound

This paper cites Self-play fine-tuning converts weak language models to strong language models.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Self-play fine-tuning converts weak language models to strong language models

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T03:44:29.885989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0d7f93e4-ae92-4d01-809f-91873f02c73f · outbound

This paper cites Constraining Statistical Isotropy using 21cm Power Spectrum and Bispectrum.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Constraining Statistical Isotropy using 21cm Power Spectrum and Bispectrum

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-07-01T14:55:48.271182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 736b2028-1a9e-4653-842f-5086b3d901ee · outbound

This paper cites Exploration–exploitation trade-off in reinforcement learning for large language models.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Exploration–exploitation trade-off in reinforcement learning for large language models

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-01T14:55:48.290623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a321ba01-88cc-4b99-8406-10ed859c32b0 · outbound

This paper cites Recursive introspection: Teaching language model agents how to self-improve.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Recursive introspection: Teaching language model agents how to self-improve

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T03:44:29.888447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation bb098042-05dc-4cbe-8b8c-d0dffae9c050 · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-07-01T14:55:48.290536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation cf5e7e3c-9472-4968-8ee3-73a547e12a96 · outbound

This paper cites Training large language models for reasoning through reverse curriculum reinforcement learning.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Training large language models for reasoning through reverse curriculum reinforcement learning

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T03:44:29.894355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e8c59bb4-f092-4d46-a3cb-c031ef797bff · outbound

This paper cites Process Reinforcement through Implicit Rewards.

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Process Reinforcement through Implicit Rewards

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-07-01T14:55:48.265222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Pith citing papers

No inbound Pith citation observations are available.