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: 220 Search mode: keyword Shortlist (0) RSS

Filter by tag

All Automatic Metrics (1,610) General (530) Long Horizon (319) Pairwise Preference (287) Coding (216) Simulation Env (186) Multi Agent (182) Medicine (115) Llm As Judge (106) Expert Verification (97) Human Eval (89) Rubric Rating (82) Web Browsing (79) Math (77) Demonstrations (67) Critique Edit (63)

Browse by Topic

Jump directly into tag and hub pages to crawl deeper content clusters.

Top Protocol Hubs

Start Here By Objective

Pick your immediate research objective and jump directly to high-signal pages, not generic search.

Benchmark Selection

Find papers with explicit benchmark anchors and comparable metric reporting.

Rater Protocol Design

Compare pairwise, rubric, and expert-verification setups before drafting your protocol.

LLM-as-Judge Setup

Start with established judge pipelines and then compare with human-eval references.

Scale Your Evaluation Team

Need human evaluators for your benchmark or preference study? OpenTrain sources pre-vetted domain experts into your annotation pipeline.

See How It Works →

QED-Nano: Teaching a Tiny Model to Prove Hard Theorems

LM-Provers, Yuxiao Qu, Amrith Setlur, Jasper Dekoninck, Edward Beeching, Jia Li · Apr 6, 2026

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 90% Moderate protocol signal Freshness: Hot Status: Ready

Rubric Rating Automatic Metrics MathCoding

To support further research on open mathematical reasoning, we release the full QED-Nano pipeline, including the QED-Nano and QED-Nano-SFT models, the FineProofs-SFT and FineProofs-RL datasets, and the training and evaluation code.

Open paper

Mitigating Distribution Sharpening in Math RLVR via Distribution-Aligned Hint Synthesis and Backward Hint Annealing

Pei-Xi Xie, Che-Yu Lin, Cheng-Lin Yang · Apr 9, 2026

Citations: 0

Match reason: Title directly matches "MATH".

Score: 80% Sparse protocol signal Freshness: Hot Status: Ready

Open paper

Don't Overthink It: Inter-Rollout Action Agreement as a Free Adaptive-Compute Signal for LLM Agents

Khushal Sethi · Apr 9, 2026

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 90% High protocol signal Freshness: Hot Status: Fallback

Automatic Metrics Long Horizon Math

We introduce TrACE (Trajectorical Adaptive Compute via agrEement), a training-free controller that allocates LLM calls adaptively across agent timesteps by measuring inter-rollout action agreement.
We evaluate TrACE against greedy decoding and fixed-budget self-consistency (SC-4, SC-8) on two benchmarks spanning single-step reasoning (GSM8K, n=50) and multi-step household navigation (MiniHouse, n=30), using a Qwen 2.5 3B Instruct…

Open paper

Cognitive Loop of Thought: Reversible Hierarchical Markov Chain for Efficient Mathematical Reasoning

Jia-Chen Zhang, Zheng Zhou, Yu-Jie Xiong · Apr 8, 2026

Citations: 0

Match reason: Title directly matches "MATH".

Score: 90% Moderate protocol signal Freshness: Hot Status: Fallback

Automatic Metrics Long Horizon Math

Inspired by human cognitive processes, we introduce a backward verification mechanism at each hierarchical layer.
Experiments on four mathematical benchmarks demonstrate the effectiveness of our method.

Open paper

SHAPE: Stage-aware Hierarchical Advantage via Potential Estimation for LLM Reasoning

Zhengyang Ai, Zikang Shan, Xiaodong Ai, Jingxian Tang, Hangkai Hu, Pinyan Lu · Apr 8, 2026

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 90% Moderate protocol signal Freshness: Hot Status: Fallback

Automatic Metrics Long Horizon Math

Extensive experiments in math reasoning across three base models and five benchmarks demonstrate that SHAPE achieves an average accuracy gain of 3% with 30% reduced token consumption.

Open paper

How Long Reasoning Chains Influence LLMs' Judgment of Answer Factuality

Minzhu Tu, Shiyu Ni, Keping Bi · Apr 8, 2026

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 87% Moderate protocol signal Freshness: Hot Status: Fallback

Human EvalAutomatic Metrics Math

Large language models (LLMs) has been widely adopted as a scalable surrogate for human evaluation, yet such judges remain imperfect and susceptible to surface-level biases.
With the rise of reasoning-capable models, exposing a generator's reasoning content to the judge provides richer information and is a natural candidate for improving judgment accuracy.

Open paper

Exclusive Unlearning

Mutsumi Sasaki, Kouta Nakayama, Yusuke Miyao, Yohei Oseki, Masaru Isonuma · Apr 7, 2026

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 83% Sparse protocol signal Freshness: Hot Status: Fallback

Red Team Math

We demonstrate that through Exclusive Unlearning, it is possible to obtain a model that ensures safety against a wide range of inputs, including jailbreaks, while maintaining the ability to respond to diverse instructions related to…

Open paper

SUPERNOVA: Eliciting General Reasoning in LLMs with Reinforcement Learning on Natural Instructions

Ashima Suvarna, Kendrick Phan, Mehrab Beikzadeh, Hritik Bansal, Saadia Gabriel · Apr 9, 2026

Citations: 0

Match reason: Matched by broad semantic/index fallback.