analiza aiqbench
Physics-Guided Policy Optimization (PGPO) introduces an information-modulated step-size multiplier that stabilizes self-distilled policy optimization by dynamically adjusting trust in self-teacher corrections. This approach preserves the theoretical guarantees of vanilla SGD while achieving significant performance gains on Science-QA datasets without incurring substantial computational overhead. The method effectively prevents training collapse in scenarios where standard self-distillation fails, offering a robust upgrade for LLM post-training pipelines.
Physics-Guided Policy Optimization with Self-Distillation
arXiv:2606.03620v1 Announce Type: cross Abstract: Self-distilled policy optimization (SDPO) has become a popular paradigm for LLM post-training, where a model learns from its own predictions conditioned on privileged information. SDPO, however, is sensitive to how much each update step should be trusted: corrections from a self-teacher can be highly informative on some batches and misleading on others, and applying them uniformly with a fixed step size can destabilize training. Drawing inspiration from viscous-fluid dynamics and formalizing the analogy at the SDE level, we propose Physics-Guided Policy Optimization (PGPO), which introduces an information-modulated step-size multiplier derived from a mutual-information estimate between the student's predictions and the feedback-conditioned teacher. We show that this modulation preserves the order-1 weak-approximation guarantees of vanilla SGD, and incurs negligible overhead per iteration. We evaluate PGPO on the Science-QA dataset, where it outperforms SDPO on 3 of the 4 domains with gains of up to +4.5 points, while remaining stable in a setting where SDPO collapses late in training.
Abstract:Self-distilled policy optimization (SDPO) has become a popular paradigm for LLM post-training, where a model learns from its own predictions conditioned on privileged information. SDPO, however, is sensitive to how much each update step should be trusted: corrections from a self-teacher can be highly informative on some batches and misleading on others, and applying them uniformly with a fixed step size can destabilize training. Drawing inspiration from viscous-fluid dynamics and formalizing the analogy at the SDE level, we propose Physics-Guided Policy Optimization (PGPO), which introduces an information-modulated step-size multiplier derived from a mutual-information estimate between the student's predictions and the feedback-conditioned teacher. We show that this modulation preserves the order-1 weak-approximation guarantees of vanilla SGD, and incurs negligible overhead per iteration. We evaluate PGPO on the Science-QA dataset, where it outperforms SDPO on 3 of the 4 domains with gains of up to +4.5 points, while remaining stable in a setting where SDPO collapses late in training.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2606.03620 [cs.LG]
(or arXiv:2606.03620v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.03620 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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