Weak-Driven Learning: How Weak Agents make Strong Agents Stronger

chenzehao, Gongxun Li, Tianxiang Ai, Yifei Li, hzx, Wang Zhou, Fuzhen Zhuang, Xianglong Liu, Jianxin Li, Deqing Wang, Yikun Ban

Weak-Driven Learning: How Weak Agents make Strong Agents Stronger: 169 upvotes on Hugging Face Daily Papers, #1 of 58 papers on 2026-02-10. Day-by-day upvote history. It lost 121 votes when the Hub removed votes in bulk.

As post-training optimization becomes central to improving large language models, we observe a persistent saturation bottleneck: once models grow highly confident, further training yields diminishing returns. While existing methods continue to reinforce target predictions, we find that informative supervision signals remain latent in models' own historical weak states. Motivated by this observation, we propose WMSS (Weak Agents Can Make Strong Agents Stronger), a post-training paradigm that leverages weak checkpoints to guide continued optimization. By identifying recoverable learning gaps via entropy dynamics and reinforcing them through compensatory learning, WMSS enables strong agents to improve beyond conventional post-training saturation. Experiments on mathematical reasoning and code generation datasets show that agents trained with our approach achieve effective performance improvements, while incurring zero additional inference cost.

Paper page on Hugging Face · arXiv

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