Skip to content

Researcher Tools

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.

Total papers: 7 Search mode: keyword RSS

Filter by tag

All Automatic Metrics (527) General (186) Long Horizon (106) Pairwise Preference (91) Coding (69) Simulation Env (67) Multi Agent (46) Medicine (35) Expert Verification (33) Llm As Judge (28) Human Eval (25) Web Browsing (25) Rubric Rating (24) Red Team (23) Critique Edit (22) Multilingual (21)
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.
BankMathBench: A Benchmark for Numerical Reasoning in Banking Scenarios

Yunseung Lee, Subin Kim, Youngjun Kwak, Jaegul Choo · Feb 19, 2026

Citations: 0
Automatic Metrics Long Horizon Math
  • However, such errors have rarely been captured by existing benchmarks.
  • Mathematical datasets focus on fundamental math problems, whereas financial benchmarks primarily target financial documents, leaving everyday banking scenarios underexplored.
GATES: Self-Distillation under Privileged Context with Consensus Gating

Alex Stein, Furong Huang, Tom Goldstein · Feb 24, 2026

Citations: 0
Automatic Metrics Long Horizon Math
  • Held-out in-domain accuracy under asymmetric evaluation improves from 46.0\% to 62.0\%, and average (maj@8) accuracy on public document-free math benchmarks improves from 20.2\% to 35.4\%.
AgentDropoutV2: Optimizing Information Flow in Multi-Agent Systems via Test-Time Rectify-or-Reject Pruning

Yutong Wang, Siyuan Xiong, Xuebo Liu, Wenkang Zhou, Liang Ding, Miao Zhang · Feb 26, 2026

Citations: 0
Automatic Metrics Multi Agent MathCoding
  • We propose AgentDropoutV2, a test-time rectify-or-reject pruning framework designed to dynamically optimize MAS information flow without retraining.
  • Empirical results on extensive math benchmarks show that AgentDropoutV2 significantly boosts the MAS's task performance, achieving an average accuracy gain of 6.3 percentage points on math benchmarks.
Duel-Evolve: Reward-Free Test-Time Scaling via LLM Self-Preferences

Sweta Karlekar, Carolina Zheng, Magnus Saebo, Nicolas Beltran-Velez, Shuyang Yu, John Bowlan · Feb 25, 2026

Citations: 0
Pairwise Preference Automatic Metrics Math
  • Building on this observation, we introduce Duel-Evolve, an evolutionary optimization algorithm that replaces external scalar rewards with pairwise preferences elicited from the same LLM used to generate candidates.
  • Results show that pairwise self-preferences provide strong optimization signal for test-time improvement over large, discrete output spaces.
Test-Time Scaling with Diffusion Language Models via Reward-Guided Stitching

Roy Miles, Aysim Toker, Andreea-Maria Oncescu, Songcen Xu, Jiankang Deng, Ismail Elezi · Feb 26, 2026

Citations: 0
Automatic Metrics Long Horizon MathCoding
  • This modular pipeline separates exploration (diffusion) from evaluation and solution synthesis, avoiding monolithic unified hybrids while preserving broad search.
  • Across math reasoning benchmarks, we find that step-level recombination is most beneficial on harder problems, and ablations highlight the importance of the final AR solver in converting stitched but imperfect rationales into accurate…

Protocol Hubs

Expert Verification Papers (32) CS.CL + Expert Verification Papers (24) Pairwise Preference Papers (89) CS.CL + Pairwise Preference Papers (74) Coding Papers (69) CS.CL Human Feedback And Eval Papers (1,020) CS.AI + Expert Verification Papers (20) CS.AI Human Feedback And Eval Papers (794) Expert Verification Or Pairwise Preference Papers (118) Pairwise Preference Papers (Last 120 Days) (59) Pairwise Preference Papers (Last 90 Days) (58) Pairwise Preference Papers (Last 60 Days) (57) Long Horizon Papers (101) CS.AI + Pairwise Preference Papers (52) Expert Verification Or Rubric Rating Papers (50) CS.CL + Coding Papers (51)

Benchmark Hubs

Metric Hubs

Daily Archives

Need human evaluators for your AI research? Scale annotation with expert AI Trainers.

Post a Job Get a Quote