Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback | Paper Citation Record

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

RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 126

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Active teacher selection for reward learning cites this paper.

Active teacher selection for reward learning Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 2

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A Roadmap to Pluralistic Alignment cites this paper.

A Roadmap to Pluralistic Alignment Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 154

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LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code cites this paper.

LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 140

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Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models cites this paper.

Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 264

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AI Safety Landscape for Large Language Models: Taxonomy, State-of-the-art, and Future Directions cites this paper.

AI Safety Landscape for Large Language Models: Taxonomy, State-of-the-art, and Future Directions Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 102

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Training Language Models to Self-Correct via Reinforcement Learning cites this paper.

Training Language Models to Self-Correct via Reinforcement Learning Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 141

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Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs cites this paper.

Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 6

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Towards Data Governance of Frontier AI Models cites this paper.

Towards Data Governance of Frontier AI Models Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 20

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ReFF: Reinforcing Format Faithfulness in Language Models across Varied Tasks cites this paper.

ReFF: Reinforcing Format Faithfulness in Language Models across Varied Tasks Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 7

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Active Inference and Human--Computer Interaction cites this paper.

Active Inference and Human--Computer Interaction Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 22

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MPPO: Multi Pair-wise Preference Optimization for LLMs with Arbitrary Negative Samples cites this paper.

MPPO: Multi Pair-wise Preference Optimization for LLMs with Arbitrary Negative Samples Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 3

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Comparing Few to Rank Many: Active Human Preference Learning using Randomized Frank-Wolfe cites this paper.

Comparing Few to Rank Many: Active Human Preference Learning using Randomized Frank-Wolfe Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 10

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AI Agent for Education: von Neumann Multi-Agent System Framework cites this paper.

AI Agent for Education: von Neumann Multi-Agent System Framework Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 35

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Many of Your DPOs are Secretly One: Attempting Unification Through Mutual Information cites this paper.

Many of Your DPOs are Secretly One: Attempting Unification Through Mutual Information Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 7

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AlphaPO: Reward Shape Matters for LLM Alignment cites this paper.

AlphaPO: Reward Shape Matters for LLM Alignment Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 4

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Stream Aligner: Efficient Sentence-Level Alignment via Distribution Induction cites this paper.

Stream Aligner: Efficient Sentence-Level Alignment via Distribution Induction Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 4

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Beyond Reward Hacking: Causal Rewards for Large Language Model Alignment cites this paper.

Beyond Reward Hacking: Causal Rewards for Large Language Model Alignment Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 11

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Debate Helps Weak-to-Strong Generalization cites this paper.

Debate Helps Weak-to-Strong Generalization Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 13

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Trustworthiness in Stochastic Systems: Towards Opening the Black Box cites this paper.

Trustworthiness in Stochastic Systems: Towards Opening the Black Box Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 84

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Offline Learning for Combinatorial Multi-armed Bandits cites this paper.

Offline Learning for Combinatorial Multi-armed Bandits Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 1

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The Energy Loss Phenomenon in RLHF: A New Perspective on Mitigating Reward Hacking cites this paper.

The Energy Loss Phenomenon in RLHF: A New Perspective on Mitigating Reward Hacking Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 7

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Process-Supervised Reinforcement Learning for Code Generation cites this paper.

Process-Supervised Reinforcement Learning for Code Generation Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 6

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Efficiently Generating Expressive Quadruped Behaviors via Language-Guided Preference Learning cites this paper.

Efficiently Generating Expressive Quadruped Behaviors via Language-Guided Preference Learning Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 22

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TruthFlow: Truthful LLM Generation via Representation Flow Correction cites this paper.

TruthFlow: Truthful LLM Generation via Representation Flow Correction Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 9

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Use of Winsome Robots for Understanding Human Feedback (UWU) cites this paper.

Use of Winsome Robots for Understanding Human Feedback (UWU) Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 25

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Probabilistic Artificial Intelligence cites this paper.

Probabilistic Artificial Intelligence Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 2013

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Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models cites this paper.

Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 12

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Thinking beyond the anthropomorphic paradigm benefits LLM research cites this paper.

Thinking beyond the anthropomorphic paradigm benefits LLM research Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 17

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Kaleidoscope Gallery: Exploring Ethics and Generative AI Through Art cites this paper.

Kaleidoscope Gallery: Exploring Ethics and Generative AI Through Art Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 11

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A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO cites this paper.

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 2024

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Reality Check: A New Evaluation Ecosystem Is Necessary to Understand AI's Real World Effects cites this paper.

Reality Check: A New Evaluation Ecosystem Is Necessary to Understand AI's Real World Effects Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 21

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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 Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 13

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Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy cites this paper.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 109

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Observation 1c0b73b2-aa5c-49b1-990e-9906ac6bb7ed · inbound

Thompson Sampling in Online RLHF with General Function Approximation cites this paper.

Thompson Sampling in Online RLHF with General Function Approximation Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 6

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Risks of AI-driven product development and strategies for their mitigation cites this paper.

Risks of AI-driven product development and strategies for their mitigation Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 88

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Multi-objective Aligned Bidword Generation Model for E-commerce Search Advertising cites this paper.

Multi-objective Aligned Bidword Generation Model for E-commerce Search Advertising Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 5

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Crowd-SFT: Crowdsourcing for LLM Alignment cites this paper.

Crowd-SFT: Crowdsourcing for LLM Alignment Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 2

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AssertBench: A Benchmark for Evaluating Self-Assertion in Large Language Models cites this paper.

AssertBench: A Benchmark for Evaluating Self-Assertion in Large Language Models Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 4

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

Exploring the Secondary Risks of Large Language Models Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 7

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Personalized Constitutionally-Aligned Agentic Superego: Secure AI Behavior Aligned to Diverse Human Values cites this paper.

Personalized Constitutionally-Aligned Agentic Superego: Secure AI Behavior Aligned to Diverse Human Values Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 4

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Collaborative Editable Model cites this paper.

Collaborative Editable Model Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 2022

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Affective-CARA: A Knowledge Graph Driven Framework for Culturally Adaptive Emotional Intelligence in HCI cites this paper.

Affective-CARA: A Knowledge Graph Driven Framework for Culturally Adaptive Emotional Intelligence in HCI Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 11

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PrefPaint: Enhancing Medical Image Inpainting through Expert Human Feedback cites this paper.

PrefPaint: Enhancing Medical Image Inpainting through Expert Human Feedback Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 6

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Lessons from a Chimp: AI "Scheming" and the Quest for Ape Language cites this paper.

Lessons from a Chimp: AI "Scheming" and the Quest for Ape Language Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 42

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Exploring a Gamified Personality Assessment Method through Interaction with LLM Agents Embodying Different Personalities cites this paper.

Exploring a Gamified Personality Assessment Method through Interaction with LLM Agents Embodying Different Personalities Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 22

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Observation ac2747d2-67f7-42a0-9154-a4e87dbab96e · inbound

Best-of-N through the Smoothing Lens: KL Divergence and Regret Analysis cites this paper.

Best-of-N through the Smoothing Lens: KL Divergence and Regret Analysis Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 12

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Observation 79a2f52d-fd27-4fbb-9f10-74f12a55cf3c · inbound

Granular feedback merits sophisticated aggregation cites this paper.

Granular feedback merits sophisticated aggregation Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 2020

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Observation 81e681ba-1912-40da-ba7b-1d2f75ac1ce6 · inbound

PrefPalette: Personalized Preference Modeling with Latent Attributes cites this paper.

PrefPalette: Personalized Preference Modeling with Latent Attributes Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 1952

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PRM-Free Security Alignment of Large Models via Red Teaming and Adversarial Training cites this paper.

PRM-Free Security Alignment of Large Models via Red Teaming and Adversarial Training Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 18

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Towards Mitigation of Hallucination for LLM-empowered Agents: Progressive Generalization Bound Exploration and Watchdog Monitor cites this paper.

Towards Mitigation of Hallucination for LLM-empowered Agents: Progressive Generalization Bound Exploration and Watchdog Monitor Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 3

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Observation e8b2e8f2-8d63-476c-91d6-e9393a28425a · inbound

RecGPT Technical Report cites this paper.

RecGPT Technical Report Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 2009

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Observation 5d235c00-dbf4-4475-9b75-70eb77925cd5 · inbound

Difficulty-Based Preference Data Selection by DPO Implicit Reward Gap cites this paper.

Difficulty-Based Preference Data Selection by DPO Implicit Reward Gap Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 30

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Observation 05948fb1-cc9e-4c62-833a-44cbbf013ac9 · inbound

Beyond Prediction: Reinforcement Learning as the Defining Leap in Healthcare AI cites this paper.

Beyond Prediction: Reinforcement Learning as the Defining Leap in Healthcare AI Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 17

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Observation dd4f5822-fdd6-4f1e-b279-7c9a34e7a1ff · inbound

FocusDPO: Dynamic Preference Optimization for Multi-Subject Personalized Image Generation via Adaptive Focus cites this paper.

FocusDPO: Dynamic Preference Optimization for Multi-Subject Personalized Image Generation via Adaptive Focus Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 3

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Observation 97bb6238-1d2c-4042-b3e8-2ea3e4dddebe · inbound

SharedRep-RLHF: A Shared Representation Approach to RLHF with Diverse Preferences cites this paper.

SharedRep-RLHF: A Shared Representation Approach to RLHF with Diverse Preferences Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 5

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Observation 204f11e3-f7e5-475c-9fe1-17de2c24ec84 · inbound

The Anti-Ouroboros Effect: Emergent Resilience in Large Language Models from Recursive Selective Feedback cites this paper.

The Anti-Ouroboros Effect: Emergent Resilience in Large Language Models from Recursive Selective Feedback Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T12:04:04.797646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95956f12-4d1f-464e-a2b7-b46706ebc560 · inbound

Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Survey cites this paper.

Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Survey Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T19:19:22.387405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2f5499a-556a-4cb9-b502-68a2b81a64ec · inbound

EAPO: Enhancing Policy Optimization with On-Demand Expert Assistance cites this paper.

EAPO: Enhancing Policy Optimization with On-Demand Expert Assistance Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T14:44:13.604742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2174ce8-2c66-4898-a84a-1f6dcd1579e8 · 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 Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T11:17:36.700516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4bec7b9b-952a-4327-a2fe-21ec7b80fcf2 · inbound

A Multifaceted Analysis of Social Biases in Large Language Models cites this paper.

A Multifaceted Analysis of Social Biases in Large Language Models Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-03T16:19:31.037475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e93959a-afe3-4f8a-977d-a8e32867b405 · inbound

Beyond Context: Large Language Models' Failure to Grasp Users' Intent cites this paper.

Beyond Context: Large Language Models' Failure to Grasp Users' Intent Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-05-16T20:11:13.642122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ee760c3f-a2e5-4896-b32f-a326426bfb41 · inbound

ETS: Energy-Guided Test-Time Scaling for Training-Free RL Alignment cites this paper.

ETS: Energy-Guided Test-Time Scaling for Training-Free RL Alignment Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 3

Resolution: metadata mismatch; local_arxiv, observed 2026-05-16T09:40:49.108927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 114285a7-d4c2-4d75-b1a5-181c265bfa08 · inbound

ETS: Energy-Guided Test-Time Scaling for Training-Free RL Alignment cites this paper.

ETS: Energy-Guided Test-Time Scaling for Training-Free RL Alignment Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 3

Resolution: metadata mismatch; local_arxiv, observed 2026-05-21T14:10:13.449970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0e13fa8f-ac8b-46b0-a53d-0f724e22b70f · inbound

Acquiring Human-Like Data-Efficient Mechanics Prediction from Deep Reinforcement Learning cites this paper.

Acquiring Human-Like Data-Efficient Mechanics Prediction from Deep Reinforcement Learning Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T06:49:03.255485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14962ea5-6d68-42a0-b81d-f9a56741eaab · inbound

After Talking with 1,000 Personas: Learning Preference-Aligned Proactive Assistants From Large-Scale Persona Interactions cites this paper.

After Talking with 1,000 Personas: Learning Preference-Aligned Proactive Assistants From Large-Scale Persona Interactions Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T04:54:12.936609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c3f6a3f-121e-4ebc-bd54-8368f0cd73ba · inbound

Meta-Learning and Meta-Reinforcement Learning -- Tracing the Path towards DeepMind's Adaptive Agent cites this paper.

Meta-Learning and Meta-Reinforcement Learning -- Tracing the Path towards DeepMind's Adaptive Agent Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 19

Resolution: metadata mismatch; local_arxiv, observed 2026-05-15T20:46:35.697989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 42c1d9f3-25d4-4821-a6a5-414e6fc24da4 · inbound

Beyond Semantic Manipulation: Token-Space Attacks on Reward Models cites this paper.

Beyond Semantic Manipulation: Token-Space Attacks on Reward Models Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:44:22.602157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8b647c86-fe95-4a56-842f-3bf8c19fe435 · inbound

The Theorems of Dr. David Blackwell and Their Contributions to Artificial Intelligence cites this paper.

The Theorems of Dr. David Blackwell and Their Contributions to Artificial Intelligence Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:44:22.602157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2e66137a-7514-48f5-b823-6564a678bd7a · inbound

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

The Art of (Mis)alignment: How Fine-Tuning Methods Effectively Misalign and Realign LLMs in Post-Training Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:44:22.602157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f2b2cbde-4eca-4447-9353-aeacf297aa06 · inbound

PlanGuard: Defending Agents against Indirect Prompt Injection via Planning-based Consistency Verification cites this paper.

PlanGuard: Defending Agents against Indirect Prompt Injection via Planning-based Consistency Verification Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:44:22.602157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 13c48fb7-dd71-4edc-8adc-4923ed9218ac · inbound

Gaslight, Gatekeep, V1-V3: Early Visual Cortex Alignment Shields Vision-Language Models from Sycophantic Manipulation cites this paper.

Gaslight, Gatekeep, V1-V3: Early Visual Cortex Alignment Shields Vision-Language Models from Sycophantic Manipulation Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 36

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T22:44:22.602157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 36751cd0-2cc4-4a3e-8169-3fa3be83a811 · inbound

AIRA: AI-Induced Risk Audit: A Structured Inspection Framework for AI-Generated Code cites this paper.

AIRA: AI-Induced Risk Audit: A Structured Inspection Framework for AI-Generated Code Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:44:22.602157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f7d9e837-e024-45cd-b794-36bf595eac1b · inbound

Policy Gradient Primal-Dual Method for Safe Reinforcement Learning from Human Feedback cites this paper.

Policy Gradient Primal-Dual Method for Safe Reinforcement Learning from Human Feedback Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 33

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T22:44:22.602157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 21963a03-975b-4c12-8210-0430445ba567 · inbound

Post-AGI Economies: Autonomy and the First Fundamental Theorem of Welfare Economics cites this paper.

Post-AGI Economies: Autonomy and the First Fundamental Theorem of Welfare Economics Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T22:44:22.602157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d85e55c0-fb36-4e7e-8db8-167d471bb613 · inbound

Three Models of RLHF Annotation: Extension, Evidence, and Authority cites this paper.

Three Models of RLHF Annotation: Extension, Evidence, and Authority Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:44:22.602157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f4fbdf6f-ea2f-4df3-9781-2c01b6941981 · inbound

Learning from Disagreement: Clinician Overrides as Implicit Preference Signals for Clinical AI in Value-Based Care cites this paper.

Learning from Disagreement: Clinician Overrides as Implicit Preference Signals for Clinical AI in Value-Based Care Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:44:22.602157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 594bfc7a-b5ee-4499-9129-3b49da21f9ec · inbound

Learning from Disagreement: Clinician Overrides as Implicit Preference Signals for Clinical AI in Value-Based Care cites this paper.

Learning from Disagreement: Clinician Overrides as Implicit Preference Signals for Clinical AI in Value-Based Care Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-05-20T23:43:50.990306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 92b09ebb-7d35-401e-935b-ef0397d63fbc · inbound

Moira: Language-driven Hierarchical Reinforcement Learning for Pair Trading cites this paper.

Moira: Language-driven Hierarchical Reinforcement Learning for Pair Trading Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:44:22.602157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 10cbd289-f5b3-4176-b54d-eef634840de4 · inbound

Efficient Preference Poisoning Attack on Offline RLHF cites this paper.

Efficient Preference Poisoning Attack on Offline RLHF Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 36

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T22:44:22.602157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7bd7c218-ff88-40ea-9006-d14104726a17 · inbound

Dr. Post-Training: A Data Regularization Perspective on LLM Post-Training cites this paper.

Dr. Post-Training: A Data Regularization Perspective on LLM Post-Training Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 124

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:44:22.602157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a7882be5-e14b-4d10-9a28-b824a2c66a79 · inbound

Curated Synthetic Data Doesn't Have to Collapse: A Theoretical Study of Generative Retraining with Pluralistic Preferences cites this paper.

Curated Synthetic Data Doesn't Have to Collapse: A Theoretical Study of Generative Retraining with Pluralistic Preferences Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 56

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T22:44:22.602157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a228f44e-35ad-4a0f-88c3-af473d5152d3 · inbound

Reinforcement Learning for Scalable and Trustworthy Intelligent Systems cites this paper.

Reinforcement Learning for Scalable and Trustworthy Intelligent Systems Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 79

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:44:22.602157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 97b9a3c1-143e-47c4-be16-64e2753cb0cd · inbound

Can Revealed Preferences Clarify LLM Alignment and Steering? cites this paper.

Can Revealed Preferences Clarify LLM Alignment and Steering? Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T22:44:22.602157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 28c1bddf-928e-457b-96f3-c92bf7e3840d · inbound

Power Reinforcement Post-Training of Text-to-Image Models with Super-Linear Advantage Shaping cites this paper.

Power Reinforcement Post-Training of Text-to-Image Models with Super-Linear Advantage Shaping Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:44:22.602157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9d69cc25-5418-41e5-97c0-3b07659707fb · inbound

Spurious Correlation Learning in Preference Optimization: Mechanisms, Consequences, and Mitigation via Tie Training cites this paper.

Spurious Correlation Learning in Preference Optimization: Mechanisms, Consequences, and Mitigation via Tie Training Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 34

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T22:44:22.602157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 91045b5c-6b56-470a-80bc-2d0ff7c0f63f · inbound

Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces cites this paper.

Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 215

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:44:22.602157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 57819ffd-b01a-45c3-895e-8b7debcb0fda · inbound

Common-agency Games for Multi-Objective Test-Time Alignment cites this paper.

Common-agency Games for Multi-Objective Test-Time Alignment Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 184

Resolution: verified exact; local_arxiv, observed 2026-05-15T06:15:06.439800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ba3bf406-2a29-4790-b9ba-99a65925cc69 · inbound

TeamTR: Trust-Region Fine-Tuning for Multi-Agent LLM Coordination cites this paper.

TeamTR: Trust-Region Fine-Tuning for Multi-Agent LLM Coordination Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 63

Resolution: metadata mismatch; local_arxiv, observed 2026-05-19T18:02:42.314028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9e08c13b-3780-4ef4-b8d5-341dd7ec9d55 · inbound

Formal Methods Meet LLMs: Auditing, Monitoring, and Intervention for Compliance of Advanced AI Systems cites this paper.

Formal Methods Meet LLMs: Auditing, Monitoring, and Intervention for Compliance of Advanced AI Systems Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-05-20T17:28:48.024488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d47a6bbf-8f01-48ce-ab89-8f3b3b24f7a1 · inbound

Preference Instability in Reward Models: Detection and Mitigation via Sparse Autoencoders cites this paper.

Preference Instability in Reward Models: Detection and Mitigation via Sparse Autoencoders Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-05-20T22:49:10.204764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 542eb250-0ca5-4b23-b1cc-734e8f95d18e · inbound

Some[Body] Must Receive That Pain for Agent Accountability cites this paper.

Some[Body] Must Receive That Pain for Agent Accountability Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-05-19T19:47:44.480439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 134094df-a128-4114-b62e-dc477c9eadb9 · inbound

ClaHF: A Human Feedback-inspired Reinforcement Learning Framework for Improving Classification Tasks cites this paper.

ClaHF: A Human Feedback-inspired Reinforcement Learning Framework for Improving Classification Tasks Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 52

Resolution: metadata mismatch; local_arxiv, observed 2026-05-20T15:13:24.966375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation da158d90-1513-4cff-b6ca-bf2fc83d9b8f · inbound

Base Models Look Human To AI Detectors cites this paper.

Base Models Look Human To AI Detectors Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 18

Resolution: metadata mismatch; local_arxiv, observed 2026-05-20T06:13:22.514511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ae5723ab-12d5-4757-9601-5cb5fb29229b · inbound

REFLECTOR: Internalizing Step-wise Reflection against Indirect Jailbreak cites this paper.

REFLECTOR: Internalizing Step-wise Reflection against Indirect Jailbreak Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 4

Resolution: metadata mismatch; local_arxiv, observed 2026-05-21T06:19:42.015118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 85dbb226-d4be-4a22-8545-5ed867158fa0 · inbound

Echo: Learning from Experience Data via User-Driven Refinement cites this paper.

Echo: Learning from Experience Data via User-Driven Refinement Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-05-22T06:41:10.792145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b4c8e3d3-9f7a-4a62-8a41-4f51d03fa8c7 · inbound

Emotional intelligence in large language models is fragmented across perception, cognition, and interaction cites this paper.

Emotional intelligence in large language models is fragmented across perception, cognition, and interaction Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 25

Resolution: metadata mismatch; local_arxiv, observed 2026-06-30T13:24:40.448632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 17887a60-a748-4acf-a1e5-5c02444d73f5 · inbound

Cultivating Machine Intelligence: The OMEGA Shift from Top-Down Optimization to Autopoietic Cognitive Ecologies cites this paper.

Cultivating Machine Intelligence: The OMEGA Shift from Top-Down Optimization to Autopoietic Cognitive Ecologies Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-06-30T00:04:06.706440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 988ba260-04d3-4ee6-a0e9-134ab4555582 · inbound

Does Capability Transfer to Subjective Behavior -- and Would Our Instruments Tell Us? A Self-Evolving, Trust-by-Construction Evaluation Paradigm cites this paper.

Does Capability Transfer to Subjective Behavior -- and Would Our Instruments Tell Us? A Self-Evolving, Trust-by-Construction Evaluation Paradigm Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-06-29T13:03:26.799213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4b911027-c79e-418a-a753-723082778d06 · inbound

In-Context Reward Adaptation for Robust Preference Modeling cites this paper.

In-Context Reward Adaptation for Robust Preference Modeling Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-06-29T08:23:15.204055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers