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

Weak-Driven Learning: How Weak Agents make Strong Agents Stronger arXiv:2602.08222v2 Announce Type: replace Abstract: 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