Skip to content
OpenTrain AIFor AI Companies

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: 5 Search mode: keyword Shortlist (0) RSS

Featured Papers

Popular high-signal papers with direct links to full protocol pages.

Browse by Topic

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

Popular Tags

Top Protocol Hubs

Weekly Eval Paper Digest

The top RLHF, evaluation, and human feedback papers — curated and summarized every Friday.

No spam. Unsubscribe anytime.

Start Here By Objective

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

Scale Your Evaluation Team

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

Safety Targeted Embedding Exploit via Refinement

Joshua Adrian Cahyono · Jul 2, 2026

Citations: 0

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

Score: 83% High protocol signal Freshness: Warm Status: Ready
Red Team Automatic Metrics CodingMultilingual
  • We show that this creates an epistemic gap in which models confidently generate harmful responses for inputs that fall outside the distribution of their safety training.
  • To study this phenomenon, we introduce STEER (Safety Targeted Embedding Exploit via Refinement), a gradient-guided attack that identifies words contributing most strongly to the model's refusal behavior and iteratively translates them into…
Open paper
Jailbreak Foundry: From Papers to Runnable Attacks for Reproducible Benchmarking

Zhicheng Fang, Jingjie Zheng, Chenxu Fu, Wei Xu · Feb 27, 2026

Citations: 0

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

Score: 78% High protocol signal Freshness: Cold Status: Ready
Red Team Llm As Judge Multi Agent CodingMultilingual
  • Jailbreak techniques for large language models (LLMs) evolve faster than benchmarks, making robustness estimates stale and difficult to compare across papers due to drift in datasets, harnesses, and judging protocols.
  • We introduce JAILBREAK FOUNDRY (JBF), a system that addresses this gap via a multi-agent workflow to translate jailbreak papers into executable modules for immediate evaluation within a unified harness.
Open paper
Stealth Fine-Tuning: Efficiently Breaking Alignment in RVLMs Using Self-Generated CoT

Le Yu, Zhengyue Zhao, Yawen Zheng, Yunhao Liu · Nov 18, 2025

Citations: 0

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

Score: 78% Moderate protocol signal Freshness: Cold Status: Ready
Automatic Metrics General
  • Reasoning-augmented Vision-Language Models (RVLMs) rely on safety alignment to prevent harmful behavior, yet their exposed chain-of-thought (CoT) traces introduce new attack surfaces.
  • In this work, we find that the safety alignment of RVLMs can be easily broken through a novel attack method termed Stealth Fine-Tuning.
Open paper
A Simple and Efficient Jailbreak Method Exploiting LLMs' Helpfulness

Xuan Luo, Yue Wang, Zefeng He, Geng Tu, Jing Li, Ruifeng Xu · Sep 17, 2025

Citations: 0

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

Score: 78% High protocol signal Freshness: Cold Status: Ready
Red Team Automatic Metrics Law
  • This study reveals a critical safety blind spot in modern LLMs: learning-style queries, which closely resemble ordinary educational questions, can reliably elicit harmful responses.
  • In addition, the assessment of defenses on the constructed safe prompts reveals inherent limitations of LLMs' safety mechanisms and flaws in the defense methods.
Open paper
Inference-time Alignment in Continuous Space

Yige Yuan, Teng Xiao, Li Yunfan, Bingbing Xu, Shuchang Tao, Yunqi Qiu · May 26, 2025

Citations: 0

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

Score: 71% Sparse protocol signal Freshness: Cold Status: Fallback
MathCoding
  • Aligning large language models with human feedback at inference time has received increasing attention due to its flexibility.
Open paper

Protocol Hubs