aiqbench analysis
This research introduces WMSS, a post-training method that utilizes weak model checkpoints to identify and recover latent learning gaps, effectively bypassing performance saturation without increasing inference costs. By leveraging entropy dynamics to reinforce compensatory learning, the approach demonstrates measurable improvements in mathematical reasoning and code generation. The technique offers a practical pathway to enhance model capabilities using existing historical states rather than requiring additional compute resources for new training data.
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 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.
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 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.
Artificial Intelligence (cs.AI)
arXiv:2602.08222 [cs.AI]
(or arXiv:2602.08222v2 [cs.AI] for this version)
Full text under the publisher's open licence · 133 words