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: 1 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.

Narrow Finetuning Leaves Clearly Readable Traces in Activation Differences

Julian Minder, Clément Dumas, Stewart Slocum, Helena Casademunt, Cameron Holmes, Robert West · Oct 14, 2025

Citations: 0

Match reason: Matched by broad semantic/index fallback.

Score: 23% Sparse protocol signal Freshness: Cold Status: Ready
General
  • We demonstrate that these analyses contain crucial information by creating an LLM-based interpretability agent to understand the finetuning domain.
  • With access to the bias, the agent performs significantly better compared to baseline agents using simple prompting.
Open paper

Protocol Hubs