Paper Citation Record · LEDGER

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities

As of 17 August 2026, this Paper Citation Record lists 100 of 117 outbound references and 3 inbound Pith citation observations for arXiv:2507.13158.

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

pith.paper-citation-record.v1
2507.13158 v1

Coverage vector

measured 100 of 117 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:34:25.301260Z

measured 103 of 103 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T11:17:36.742733Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T07:59:40.152268Z

Reference resolution

100 of 117 outbound references displayed

verified exact5
verified fuzzy0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 77084717-f2be-497f-8d93-392cf620e010 · outbound

This paper cites GPT-4 Technical Report.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities GPT-4 Technical Report

Reference 1

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Unresolved cited work

Reference 7

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This paper cites Language models are few-shot learners.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Language models are few-shot learners

Reference 8

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This paper cites PAD: Personalized Alignment of LLMs at Decoding-Time.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities PAD: Personalized Alignment of LLMs at Decoding-Time

Reference 11

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This paper cites Reward Model Ensembles Help Mitigate Overoptimization.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Reward Model Ensembles Help Mitigate Overoptimization

Reference 13

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This paper cites SEE: Strategic Exploration and Exploitation for Cohesive In-Context Prompt Optimization.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities SEE: Strategic Exploration and Exploitation for Cohesive In-Context Prompt Optimization

Reference 14

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Observation 6761f935-4f6b-4abd-97c8-6a9572c0cc0d · outbound

This paper cites A Survey of Automatic Prompt Optimization with Instruction-focused Heuristic-based Search Algorithm.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities A Survey of Automatic Prompt Optimization with Instruction-focused Heuristic-based Search Algorithm

Reference 15

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

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 17

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This paper cites RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment

Reference 18

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Unresolved cited work

Reference 19

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Observation c99d0c59-2b0a-4352-94b3-00b75f366909 · outbound

This paper cites PaLM-E: An Embodied Multimodal Language Model.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities PaLM-E: An Embodied Multimodal Language Model

Reference 20

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Observation 3c01026a-8ea6-45ac-b721-2b1569ef96ae · outbound

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

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators

Reference 21

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

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities KTO: Model Alignment as Prospect Theoretic Optimization

Reference 22

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This paper cites Self-Attentional Credit Assignment for Transfer in Reinforcement Learning.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Self-Attentional Credit Assignment for Transfer in Reinforcement Learning

Reference 24

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Observation 6f8a58f3-a49d-42a4-b821-d9b72e930c01 · outbound

This paper cites Learning Robust Rewards with Adversarial Inverse Reinforcement Learning.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Learning Robust Rewards with Adversarial Inverse Reinforcement Learning

Reference 25

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This paper cites BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling

Reference 27

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

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 28

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This paper cites EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers

Reference 29

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Observation 2e45fe2a-0649-4a2e-88a4-cf1875fbf5d2 · outbound

This paper cites Direct Language Model Alignment from Online AI Feedback.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Direct Language Model Alignment from Online AI Feedback

Reference 30

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Unresolved cited work

Reference 31

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Soft Actor-Critic Algorithms and Applications

Reference 32

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Prompt-to-Prompt Image Editing with Cross Attention Control

Reference 34

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Explaining Length Bias in LLM-Based Preference Evaluations

Reference 36

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Large Language Models Cannot Self-Correct Reasoning Yet

Reference 37

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This paper cites Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback

Reference 38

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities OpenAI o1 System Card

Reference 39

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This paper cites Towards Efficient Exact Optimization of Language Model Alignment.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Towards Efficient Exact Optimization of Language Model Alignment

Reference 40

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Scaling Laws for Neural Language Models

Reference 41

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities ARGS: Alignment as Reward-Guided Search

Reference 43

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Decomposed Prompting: A Modular Approach for Solving Complex Tasks

Reference 44

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes

Reference 45

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities DiffWave: A Versatile Diffusion Model for Audio Synthesis

Reference 46

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Privacy in Large Language Models: Attacks, Defenses and Future Directions

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Personalized Language Modeling from Personalized Human Feedback

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Reward-Guided Speculative Decoding for Efficient LLM Reasoning

Reference 49

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities 24 (AAAI 2025 and ACL

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Statistical Rejection Sampling Improves Preference Optimization

Reference 52

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities RRM: Robust Reward Model Training Mitigates Reward Hacking

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 54

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Inference-time scaling for generalist reward modeling.arXiv preprint arXiv:2504.02495,

Reference 55

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Rethinking Diverse Human Preference Learning through Principal Component Analysis

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Generative Reward Models

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities A Survey of Explainable Reinforcement Learning

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Playing Atari with Deep Reinforcement Learning

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Active Preference Learning for Large Language Models

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Unresolved cited work

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This paper cites Deep Research is a new agentic AI capability integrated within ChatGPT that autonomously conducts multi-step web research and synthesizes comprehensive reports.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Deep Research is a new agentic AI capability integrated within ChatGPT that autonomously conducts multi-step web research and synthesizes comprehensive reports

Reference 62

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

Reference 63

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 65

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 67

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challenges

Reference 68

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Direct Preference Optimization: Your Language Model is Secretly a Reward Model

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Observation 1ceb9cd7-bbbc-49c6-a586-76b6bd52e915 · outbound

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Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Unresolved cited work

Reference 70

Resolution: parse uncertain; no resolver link, observed 2026-08-06T16:34:25.149674Z

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Observation 87501923-c62b-4545-b5ef-5c4fd60de35e · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.152709Z

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Observation cd2fe72f-dacb-4593-a527-847122627c44 · outbound

This paper cites A critical look at tokenwise reward-guided text generation.arXiv preprint arXiv:2406.07780,.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities A critical look at tokenwise reward-guided text generation.arXiv preprint arXiv:2406.07780,

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Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.155797Z

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Observation 8e80c9d6-ee00-4950-b41e-170613f0611b · outbound

This paper cites A Generalist Agent.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities A Generalist Agent

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.159876Z

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Observation a2502d7e-e280-42d3-b361-6ba68091870a · outbound

This paper cites Learning Long-Term Reward Redistribution via Randomized Return Decomposition.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Learning Long-Term Reward Redistribution via Randomized Return Decomposition

Reference 74

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:34:25.818920Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 21ae2890-0204-44ba-b4bf-2b3cff27a916 · outbound

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

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities High-Dimensional Continuous Control Using Generalized Advantage Estimation

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.170316Z

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Observation c615db8a-b98d-4bb0-aa2a-419d1f96996b · outbound

This paper cites Spurious Rewards: Rethinking Training Signals in RLVR.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Spurious Rewards: Rethinking Training Signals in RLVR

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.176219Z

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Observation 86484ea3-4055-4753-a933-a2e3a05de87c · outbound

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

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.179391Z

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Observation 6f256eb6-64ed-4697-934f-42d9e1153fb3 · outbound

This paper cites Reviving The Classics: Active Reward Modeling in Large Language Model Alignment.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Reviving The Classics: Active Reward Modeling in Large Language Model Alignment

Reference 80

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:34:25.767096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 08498943-cdf1-4c8e-a21a-b4efbf1bb866 · outbound

This paper cites Understanding the Performance Gap in Preference Learning: A Dichotomy of RLHF and DPO.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Understanding the Performance Gap in Preference Learning: A Dichotomy of RLHF and DPO

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.185554Z

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Observation b622793b-3d91-4a6e-b0db-24460636a79a · outbound

This paper cites When Life Gives You Lemons, Make Cherryade: Converting Feedback from Bad Responses into Good Labels.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities When Life Gives You Lemons, Make Cherryade: Converting Feedback from Bad Responses into Good Labels

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.188545Z

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Observation ae7730dd-9eb7-48e4-a4de-b85e157906e8 · outbound

This paper cites an unresolved cited work.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Unresolved cited work

Reference 83

Resolution: parse uncertain; no resolver link, observed 2026-08-06T16:34:25.192087Z

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Observation c36e4d87-d47b-44e8-8317-feb967e0fa88 · outbound

This paper cites Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.194886Z

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Observation 129db947-a33a-4513-93e2-67f981934587 · outbound

This paper cites Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHF.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHF

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.197977Z

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Observation 6e9d55e6-b139-4cc8-a47c-3cd574ed1282 · outbound

This paper cites Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.201081Z

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Observation 49f8f937-525f-4198-9dfc-d182860057d2 · outbound

This paper cites A Roadmap to Pluralistic Alignment.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities A Roadmap to Pluralistic Alignment

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.204086Z

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Observation 299da6d6-f1b3-43da-9533-97f5226241fe · outbound

This paper cites Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.210425Z

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Observation 1b8a5cf1-704d-4ee7-9611-ac9b10123401 · outbound

This paper cites Rethinking Bradley-Terry Models in Preference-Based Reward Modeling: Foundations, Theory, and Alternatives.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Rethinking Bradley-Terry Models in Preference-Based Reward Modeling: Foundations, Theory, and Alternatives

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.213454Z

Source-reported events for the cited work

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Observation 36944cbd-6b9a-4553-9207-488a235ed765 · outbound

This paper cites Learning to Repair: Repairing model output errors after deployment using a dynamic memory of feedback.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Learning to Repair: Repairing model output errors after deployment using a dynamic memory of feedback

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.220429Z

Source-reported events for the cited work

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Observation bbeeeba4-80c0-4693-9d85-79b85eef1422 · outbound

This paper cites DeepMind Control Suite.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities DeepMind Control Suite

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.223363Z

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Observation 5c3029af-049b-429f-a029-722c15b9e226 · outbound

This paper cites Grpo: Generalized reinforcement preference optimization.arXiv preprint arXiv:2405.00000,.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Grpo: Generalized reinforcement preference optimization.arXiv preprint arXiv:2405.00000,

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.226345Z

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Observation 2fef2af8-216f-45a4-9f61-02b0e6243bcd · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.230052Z

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Observation 83ad950e-a994-42d4-a596-dd77890634e7 · outbound

This paper cites Gram: A generative foundation reward model for reward generalization.arXiv preprint arXiv:2506.14175, 2025a.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Gram: A generative foundation reward model for reward generalization.arXiv preprint arXiv:2506.14175, 2025a

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.233479Z

Source-reported events for the cited work

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Observation 02dac0dd-db99-4c97-ae6b-d9eee97d771c · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.236262Z

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Observation f0519097-48ef-4274-8731-66dc94072c1e · outbound

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

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.239464Z

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Observation b81d8143-0d29-4284-9d02-6ba928991711 · outbound

This paper cites How to Leverage Demonstration Data in Alignment for Large Language Model? A Self-Imitation Learning Perspective.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities How to Leverage Demonstration Data in Alignment for Large Language Model? A Self-Imitation Learning Perspective

Reference 100

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:34:25.472318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation caa7a167-a786-40b8-ad11-8e33e6c466dd · outbound

This paper cites Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint

Reference 101

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.249776Z

Source-reported events for the cited work

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Observation 11c90913-72bb-4836-a257-2e2879251b27 · outbound

This paper cites A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 102

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.253465Z

Source-reported events for the cited work

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Observation 34aa152a-be09-43ba-852b-232695cee304 · outbound

This paper cites Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models

Reference 103

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.256725Z

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Observation 5809d262-c85c-43f6-ae1f-2ecea71cf6bc · outbound

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

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study

Reference 104

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.260209Z

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Observation 738a22bb-d8db-4848-92be-c26a1f7ba4ff · outbound

This paper cites Large Language Models as Optimizers.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Large Language Models as Optimizers

Reference 105

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.263190Z

Source-reported events for the cited work

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Observation a357dcd6-bad0-41da-8319-1a491f2a2c77 · outbound

This paper cites Rethinking Goal-conditioned Supervised Learning and Its Connection to Offline RL.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Rethinking Goal-conditioned Supervised Learning and Its Connection to Offline RL

Reference 106

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.266194Z

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Observation b248e687-6a40-4976-a20b-c4b0a2463933 · outbound

This paper cites Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs

Reference 107

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.269350Z

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Observation ac69e7f0-10ed-4d9b-8ad0-9b06eba55910 · outbound

This paper cites Relative Preference Optimization: Enhancing LLM Alignment through Contrasting Responses across Identical and Diverse Prompts.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Relative Preference Optimization: Enhancing LLM Alignment through Contrasting Responses across Identical and Diverse Prompts

Reference 108

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.272362Z

Source-reported events for the cited work

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Observation 0daaf2f3-714b-4697-b4c1-8ef1564e3f8b · outbound

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

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 109

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.276239Z

Source-reported events for the cited work

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Observation 26a849c7-7e62-4a1a-9237-5698fd209cb9 · outbound

This paper cites RRHF: Rank Responses to Align Language Models with Human Feedback without tears.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities RRHF: Rank Responses to Align Language Models with Human Feedback without tears

Reference 110

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.279111Z

Source-reported events for the cited work

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Observation c79daecb-c563-4a67-b965-4f7e470b53a2 · outbound

This paper cites Diversifying AI: Towards Creative Chess with AlphaZero.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Diversifying AI: Towards Creative Chess with AlphaZero

Reference 111

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.282653Z

Source-reported events for the cited work

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Observation 1b131a45-5c49-4f61-b78d-06947941fe55 · outbound

This paper cites When scaling meets LLM finetuning: The effect of data, model and finetuning method.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities When scaling meets LLM finetuning: The effect of data, model and finetuning method

Reference 112

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.285823Z

Source-reported events for the cited work

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Observation 963c5887-8ece-4b8d-8c6c-f0a2e45def27 · outbound

This paper cites Generative Verifiers: Reward Modeling as Next-Token Prediction.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Generative Verifiers: Reward Modeling as Next-Token Prediction

Reference 113

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.288762Z

Source-reported events for the cited work

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Observation d3396fdf-285c-4f06-9ab9-8ce1b7348ab2 · outbound

This paper cites Overcoming Reward Overoptimization via Adversarial Policy Optimization with Lightweight Uncertainty Estimation.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Overcoming Reward Overoptimization via Adversarial Policy Optimization with Lightweight Uncertainty Estimation

Reference 114

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.291921Z

Source-reported events for the cited work

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Observation 4730a3b9-3f73-48fe-bbaa-275bc9827592 · outbound

This paper cites Secrets of RLHF in Large Language Models Part I: PPO.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Secrets of RLHF in Large Language Models Part I: PPO

Reference 115

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.294914Z

Source-reported events for the cited work

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Observation 99c591d9-b88d-4fbb-b681-f880534ff6fc · outbound

This paper cites DPO Meets PPO: Reinforced Token Optimization for RLHF.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities DPO Meets PPO: Reinforced Token Optimization for RLHF

Reference 116

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.297970Z

Source-reported events for the cited work

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Observation ccac6194-08ff-4523-a40f-e40eac577756 · outbound

This paper cites Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Reference 117

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.301260Z

Source-reported events for the cited work

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Observation 90b10786-5de4-4378-88bc-07dde36cb0ba · outbound

This paper cites RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Reference 1952

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:24.945869Z

Source-reported events for the cited work

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Observation 3f63be04-b7ef-4d04-826a-b31d7e63bd06 · outbound

This paper cites Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Reference 1973

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.207451Z

Source-reported events for the cited work

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Observation 3889c48b-10e2-4ab4-bbfb-46d8a41f0f78 · outbound

This paper cites Retrieval Augmented Thought Process for Private Data Handling in Healthcare.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Retrieval Augmented Thought Process for Private Data Handling in Healthcare

Reference 1991

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.136743Z

Source-reported events for the cited work

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Observation f8c77f3b-3746-4371-abd6-e3bb2c761ef8 · outbound

This paper cites Style Over Substance: Evaluation Biases for Large Language Models.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Style Over Substance: Evaluation Biases for Large Language Models

Reference 1992

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.242829Z

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Pith citing papers

Observation 58ce233a-7689-4a88-977a-8ab27b2358d0 · inbound

The Alignment Auditor: A Bayesian Framework for Verifying and Refining LLM Objectives cites this paper.

The Alignment Auditor: A Bayesian Framework for Verifying and Refining LLM Objectives Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-04T11:17:36.742733Z

Source-reported events for the cited work

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Observation 2654dec6-ac02-4a04-9249-fd87fe4db4ce · inbound

The Alignment Flywheel: A Governance-Centric Hybrid MAS for Architecture-Agnostic Safety cites this paper.

The Alignment Flywheel: A Governance-Centric Hybrid MAS for Architecture-Agnostic Safety Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:06:30.621395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 72c87fdd-80a0-4329-9873-91904335d9f2 · inbound

Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learning cites this paper.

Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learning Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities

Reference 188

Resolution: verified exact; arxiv_id, observed 2026-07-04T07:59:40.153902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.