Reproduce “Before the Next Rollout: Diagnosing Skill Utility Shifts from Policy Updates” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Yifan Wang, Zhenyi Gai, Yiwen Zhao, Mingxuan Jiang, Keyang Chen, Zeping Li, Hongfeng Chai, Yixin Cao
The authors introduce a readout to rank reusable skills for reassessment after a policy update. It scores old and new policies on recorded contexts, comparing each skill with a matched control, then uses training advantages to give preference shifts reward direction. Across six benchmarks, reward-directed readouts achieve 0.802 AUROC versus 0.525 for magnitude-only readouts, distinguishing utility declines from increases, not estimating exact utility changes.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.