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Human Feedback and Eval Paper Explorer

A focused feed for RLHF, preference data, rater protocols, agent evaluation, and LLM-as-judge research. Every paper includes structured metadata for quick triage.

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Stop-Think-AutoRegress: Language Modeling with Latent Diffusion Planning

Justin Lovelace, Christian Belardi, Sofian Zalouk, Adhitya Polavaram, Srivatsa Kundurthy, Kilian Q. Weinberger · Feb 24, 2026

Citations: 0
Llm As JudgeAutomatic Metrics General
  • Evaluations show STAR-LDM significantly outperforms similar-sized models on language understanding benchmarks and achieves >70\% win rates in LLM-as-judge comparisons for narrative coherence and commonsense reasoning.
VolleyBots: A Testbed for Multi-Drone Volleyball Game Combining Motion Control and Strategic Play

Zelai Xu, Ruize Zhang, Chao Yu, Huining Yuan, Xiangmin Yi, Shilong Ji · Feb 4, 2025

Citations: 0
Demonstrations Automatic MetricsSimulation Env Multi Agent General
  • We provide a comprehensive suite of tasks ranging from single-drone drills to multi-drone cooperative and competitive tasks, accompanied by baseline evaluations of representative reinforcement learning (RL), multi-agent reinforcement…
  • Simulation results show that on-policy RL methods outperform off-policy methods in single-agent tasks, but both approaches struggle in complex tasks that combine motion control and strategic play.
Toward Ultra-Long-Horizon Agentic Science: Cognitive Accumulation for Machine Learning Engineering

Xinyu Zhu, Yuzhu Cai, Zexi Liu, Bingyang Zheng, Cheng Wang, Rui Ye · Jan 15, 2026

Citations: 0
Simulation Env Long Horizon General
  • The advancement of artificial intelligence toward agentic science is currently bottlenecked by the challenge of ultra-long-horizon autonomy, the ability to sustain strategic coherence and iterative correction over experimental cycles spanni
  • Here, we present ML-Master 2.0, an autonomous agent that masters ultra-long-horizon machine learning engineering (MLE) which is a representative microcosm of scientific discovery.
Gradient Regularization Prevents Reward Hacking in Reinforcement Learning from Human Feedback and Verifiable Rewards

Johannes Ackermann, Michael Noukhovitch, Takashi Ishida, Masashi Sugiyama · Feb 20, 2026

Citations: 0
Llm As JudgeAutomatic Metrics Math
  • Reinforcement Learning from Human Feedback (RLHF) or Verifiable Rewards (RLVR) are two key steps in the post-training of modern Language Models (LMs).
  • GR achieves a higher GPT-judged win-rate in RLHF, avoids overly focusing on the format in rule-based math rewards, and prevents hacking the judge in LLM-as-a-Judge math tasks.
Robust Preference Alignment via Directional Neighborhood Consensus

Ruochen Mao, Yuling Shi, Xiaodong Gu, Jiaheng Wei · Oct 23, 2025

Citations: 0
Pairwise Preference Automatic Metrics General
  • To address this challenge, we introduce Robust Preference Selection (RPS), a post-hoc, training-free method by leveraging directional neighborhood consensus.
  • Comprehensive experiments across three distinct alignment paradigms (DPA, DPO, and SFT) demonstrate that RPS consistently improves robustness against this baseline, achieving win rates of up to 69% on challenging preferences from…
A Scalable Framework for Evaluating Health Language Models

Neil Mallinar, A. Ali Heydari, Xin Liu, Anthony Z. Faranesh, Brent Winslow, Nova Hammerquist · Mar 30, 2025

Citations: 0
Rubric RatingExpert Verification Automatic Metrics Medicine
  • As LLM-driven health applications are increasingly adopted, rigorous and efficient one-sided evaluation methodologies are crucial to ensure response quality across multiple dimensions, including accuracy, personalization and safety.
  • In this work, we introduce Adaptive Precise Boolean rubrics: an evaluation framework that streamlines human and automated evaluation of open-ended questions by identifying gaps in model responses using a minimal set of targeted rubrics…

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