Self-Distilled Policy Gradient
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Computer Science > Machine Learning
Title:Self-Distilled Policy Gradient
Abstract:On-policy self-distillation, where a language model conditions on privileged context to supervise its own generations, is a promising source of dense supervision for sparse-reward reinforcement learning. Actually, it can be instantiated as an auxiliary full-vocabulary student-to-teacher reverse Kullback-Leibler divergence loss. We therefore propose SDPG, a self-distilled policy-gradient framework that combines group-relative verifier advantages with normalized standard deviation, exact full-vocabulary on-policy self-distillation, as well as reference-policy KL regularization. Empirically, SDPG improves stability and performance over RLVR and self-distillation baselines. The code is available at this https URL.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.04036 [cs.LG] |
| (or arXiv:2606.04036v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.04036
arXiv-issued DOI via DataCite (pending registration)
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