Skip to content

OpenTrain Research 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: 8 Search mode: keyword RSS

Filter by tag

All Automatic Metrics (978) General (590) Coding (314) Simulation Env (115) Math (103) Multilingual (99) Long Horizon (82) Medicine (78) Pairwise Preference (70) Law (45) Multi Agent (41) Human Eval (38) Expert Verification (25) Web Browsing (22) Critique Edit (21) Red Team (21)
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.
ToolMATH: A Math Tool Benchmark for Realistic Long-Horizon Multi-Tool Reasoning

Hyeonje Choi, Jeongsoo Lee, Hyojun Lee, Jay-Yoon Lee · Feb 24, 2026

Citations: 0
Simulation Env Long Horizon Math
  • We introduce \ToolMATH, a math-grounded benchmark that evaluates tool-augmented language models in realistic multi-tool environments where the output depends on calling schema-specified tools and sustaining multi-step execution.
  • It turns math problems into a controlled, correctness-checkable benchmark with tool sets, enabling systematic evaluation of model reliability under (1) large, overlapping tool catalogs and (2) the absence of the intended capability.
Beyond Single-Turn: A Survey on Multi-Turn Interactions with Large Language Models

Yubo Li, Xiaobin Shen, Xinyu Yao, Xueying Ding, Yidi Miao, Ramayya Krishnan · Apr 7, 2025

Citations: 0
Red Team Automatic Metrics Math
  • We organize existing benchmarks and datasets into coherent categories reflecting the evolving landscape of multi-turn dialogue evaluation, and review a broad spectrum of enhancement methodologies, including model-centric strategies (in-cont
Document Reconstruction Unlocks Scalable Long-Context RLVR

Yao Xiao, Lei Wang, Yue Deng, Guanzheng Chen, Ziqi Jin, Jung-jae Kim · Feb 9, 2026

Citations: 0
Rubric Rating Automatic Metrics Coding
  • However, it often relies on gold-standard answers or explicit evaluation rubrics provided by powerful teacher models or human experts, which are costly and time-consuming.
  • In this work, we investigate unsupervised approaches to enhance the long-context capabilities of LLMs, eliminating the need for heavy human annotations or teacher models' supervision.
Citations: 0
Simulation Env Long Horizon Coding
  • While Large Language Models (LLMs) have demonstrated strong zero-shot reasoning capabilities, their deployment as embodied agents still faces fundamental challenges in long-horizon planning.
  • Unlike open-ended text generation, embodied agents must decompose high-level intent into actionable sub-goals while strictly adhering to the logic of a dynamic, observed environment.
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.
Beyond Fact Retrieval: Episodic Memory for RAG with Generative Semantic Workspaces

Shreyas Rajesh, Pavan Holur, Chenda Duan, David Chong, Vwani Roychowdhury · Nov 10, 2025

Citations: 0
Automatic Metrics Long Horizon Coding
  • On the Episodic Memory Benchmark (EpBench) \cite{huet_episodic_2025} comprising corpora ranging from 100k to 1M tokens in length, GSW outperforms existing RAG based baselines by up to \textbf{20\%}.
  • More broadly, GSW offers a concrete blueprint for endowing LLMs with human-like episodic memory, paving the way for more capable agents that can reason over long horizons.

Protocol Hubs

Expert Verification Papers (25) CS.CL + Expert Verification Papers (20) Pairwise Preference Papers (70) CS.CL + Pairwise Preference Papers (62) CS.AI + Expert Verification Papers (15) CS.AI + Pairwise Preference Papers (42) Rubric Rating Papers (17) CS.CL + Rubric Rating Papers (16) General + Pairwise Preference Papers (43) Expert Verification Or Rubric Rating Papers (39) CS.CL + Math Papers (84) Long Horizon Papers (82) CS.CL + Human Eval Papers (35) CS.CL + Long Horizon Papers (58) Expert Verification + Medicine Papers (11) Human Eval Papers (38)

Benchmark Hubs

Metric Hubs

Daily Archives