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Across unseen environments, SPA consistently and substantially improves over vanilla RL: for example, it raises the Sokoban success rate from 25.6% to 59.8% on Qwen2.5-1.5B-Instruct, letting sub-3B models surpass a 20B baseline on these tasks.
HypoSearch is proposed, which generates lightweight hypotheses as soft search hints, explores them through bounded independent branches, and compares branch-level evidence before commitment, and consistently outperforms single-trajectory search and standard parallel baselines.
HarnessCompass is proposed, a novel automatic harness evolution framework built around constrained evolution, proactive feedback, and component-wise optimization that improves Pass@1 from 54\% to 66\% in only 5 evolution iterations, outperforming AHE in both effectiveness and evolution efficiency.
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