Asis-GRPO is a reinforcement-learning continuation of
Qwen3.5-35B-A3B-Asis (a web-search-agent SFT of
Qwen/Qwen3.5-35B-A3B) trained with
turn-level GRPO on stop-decision pivot states (PivotRL-style). Instead of optimizing whole-episode returns, the policy is optimized specifically at the decision points where an agent chooses between
stopping to answer and
continuing to search — the locus of over-reflection. Qualitatively, the released checkpoint
maintains baseline accuracy while substantially reducing post-answer over-search.
The RL run was intentionally trained past its useful region and swept checkpoint-by-checkpoint on downstream browsing-agent evaluation. The released step (~32) is the accuracy-neutral over-search-reduction point: the latest checkpoint at which end-task accuracy is statistically indistinguishable from the SFT baseline while post-answer over-search (continued searching after the answer has been stated) is significantly reduced. Later checkpoints trade accuracy or healthy verification behavior for further efficiency and were not released.
Research artifact. Intended for studying stop-decision RL, over-search reduction, and the accuracy-efficiency frontier of tool-using web agents; also usable as a more search-efficient drop-in for the Asis SFT arm on the same tool surface (function-calling tools for web search, page opening, and in-page lookup).
