Paper Citation Record · LEDGER

A Minimaximalist Approach to Reinforcement Learning from Human Feedback

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 17 inbound Pith citation observations for arXiv:2401.04056.

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

pith.paper-citation-record.v1
2401.04056 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T19:40:42.044924Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T19:40:06.225176Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation e858087c-4f0c-4fdb-a226-b81725be73f4 · inbound

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

KTO: Model Alignment as Prospect Theoretic Optimization A Minimaximalist Approach to Reinforcement Learning from Human Feedback

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-12T12:17:53.574466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1daba942-17dd-4a26-bfe4-8731dcd449fd · inbound

Design Considerations in Offline Preference-based RL cites this paper.

Design Considerations in Offline Preference-based RL A Minimaximalist Approach to Reinforcement Learning from Human Feedback

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-08T19:40:42.044924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba2d89d1-1192-451c-b3d4-1c5f0e0aab1b · inbound

Incentivize without Bonus: Provably Efficient Model-based Online Multi-agent RL for Markov Games cites this paper.

Incentivize without Bonus: Provably Efficient Model-based Online Multi-agent RL for Markov Games A Minimaximalist Approach to Reinforcement Learning from Human Feedback

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T20:38:32.922229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 528e43c5-8c35-4cc0-81c2-71cccdb87003 · inbound

Square$\chi$PO: Differentially Private and Robust $\chi^2$-Preference Optimization in Offline Direct Alignment cites this paper.

Square$\chi$PO: Differentially Private and Robust $\chi^2$-Preference Optimization in Offline Direct Alignment A Minimaximalist Approach to Reinforcement Learning from Human Feedback

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-07T13:45:01.173114Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd7f7b4b-28b6-43e8-b9aa-3ae3317ff2c8 · inbound

Distortion of AI Alignment: Does Preference Optimization Optimize for Preferences? cites this paper.

Distortion of AI Alignment: Does Preference Optimization Optimize for Preferences? A Minimaximalist Approach to Reinforcement Learning from Human Feedback

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T12:49:10.847272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d722aab7-75a3-4e2c-b6c7-a7e5fb01397f · inbound

e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs cites this paper.

e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs A Minimaximalist Approach to Reinforcement Learning from Human Feedback

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:28.528670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16a5bab6-50fb-4ee9-8ab7-5debbd946e2c · inbound

Game Theory Meets LLM and Agentic AI: Reimagining Cybersecurity for the Age of Intelligent Threats cites this paper.

Game Theory Meets LLM and Agentic AI: Reimagining Cybersecurity for the Age of Intelligent Threats A Minimaximalist Approach to Reinforcement Learning from Human Feedback

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T17:53:19.732489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc2264f5-4b18-4e15-8750-cca5d7658c02 · 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 A Minimaximalist Approach to Reinforcement Learning from Human Feedback

Reference 157

Resolution: unresolved; no resolver link, observed 2026-08-04T16:07:40.529374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee96b49f-3cc8-47e0-9d31-f77649e93a17 · inbound

Reward Shaping for (Inference-Time) Alignment: A Stackelberg Game Perspective cites this paper.

Reward Shaping for (Inference-Time) Alignment: A Stackelberg Game Perspective A Minimaximalist Approach to Reinforcement Learning from Human Feedback

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T06:06:23.941662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b11854d4-cec3-48d3-a19b-07aaa8f37f4c · inbound

Why Does Agentic Safety Fail to Generalize Across Tasks? cites this paper.

Why Does Agentic Safety Fail to Generalize Across Tasks? A Minimaximalist Approach to Reinforcement Learning from Human Feedback

Reference 103

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:06:00.273725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 98ceb732-b196-4dd3-a71f-c97c274c37f3 · inbound

Team-Based Self-Play With Dual Adaptive Weighting for Fine-Tuning LLMs cites this paper.

Team-Based Self-Play With Dual Adaptive Weighting for Fine-Tuning LLMs A Minimaximalist Approach to Reinforcement Learning from Human Feedback

Reference 68

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:46:31.191814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 43f040d0-a16a-423f-9405-13456cb68b72 · inbound

TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching cites this paper.

TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching A Minimaximalist Approach to Reinforcement Learning from Human Feedback

Reference 155

Resolution: verified exact; arxiv_id, observed 2026-05-13T04:57:17.360108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 055acd68-30f0-470a-b6b4-38d503007950 · inbound

TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching cites this paper.

TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching A Minimaximalist Approach to Reinforcement Learning from Human Feedback

Reference 155

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:45:06.540244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0789e877-01c2-4fa2-9f50-2a28bbbd37dc · inbound

MAPL: Multi-Objective Preference Learning for Robot Locomotion cites this paper.

MAPL: Multi-Objective Preference Learning for Robot Locomotion A Minimaximalist Approach to Reinforcement Learning from Human Feedback

Reference 30

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T19:40:06.226832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d35339d3-9b4b-4251-a48b-9d7aa3a6ede3 · inbound

Multi-Turn On-Policy Distillation with Prefix Replay cites this paper.

Multi-Turn On-Policy Distillation with Prefix Replay A Minimaximalist Approach to Reinforcement Learning from Human Feedback

Reference 187

Resolution: unresolved; no resolver link, observed 2026-07-11T13:53:36.775836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5a3a907-f792-423e-8385-c14807685fc2 · inbound

Multi-Turn On-Policy Distillation with Prefix Replay cites this paper.

Multi-Turn On-Policy Distillation with Prefix Replay A Minimaximalist Approach to Reinforcement Learning from Human Feedback

Reference 188

Resolution: unresolved; no resolver link, observed 2026-08-02T08:40:54.387397Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28d11439-d28d-4cf4-9178-6e6ffaba6eb9 · inbound

Visual Token Compression Enhances Robustness of MLLMs cites this paper.

Visual Token Compression Enhances Robustness of MLLMs A Minimaximalist Approach to Reinforcement Learning from Human Feedback

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-01T13:10:37.604919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.