A Predictive Law for On-Policy Self-Distillation From World Feedback
Moving beyond simple scalar rewards toward richer world feedback is a natural path to more scalable RL post-training. On-policy self-distillation (OPSD) is a promising recent approach that uses arbitrary feedback as learning signal, yet its reliability compared to established methods, such as GRPO, remains unclear. We identify a strikingly consistent linear correlation between the initial student-self-teacher performance gap and the final performance improvement in OPSD. This relationship holds across context types and model families, providing a powerful predictive law for anticipating the outcome of an OPSD configuration without running the full training procedure. Interestingly, we show that this linear predictability holds with model scale, suggesting a potential basis for new empirical scaling laws on larger models with stronger in-context learning capabilities. In essence, our findings show that OPSD performance can be predicted and tuned before training, offering a principled way to incorporate world feedback as a first-class component of the post-training pipeline.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Learning from Language Feedback via Variational Policy Distillation
Reinforcement learning from verifiable rewards (RLVR) suffers from sparse outcome signals, creating severe exploration bottlenecks on complex reasoning tasks. Recent on-policy self-distillation methods attempt to address…
Reinforcement LearningMathematical ReasoningCode GenerationBe My Tutor: On-Policy Co-Distillation for Mutual LLM Improvement via Peer Feedback
We study multi-domain LLM training in which two models, each stronger in a different domain, co-evolve by tutoring each other through on-policy feedback. Unlike one-way distillation or single-model fine-tuning, our goal …
On-Policy Self-Distillation with Sampled Demonstrations Reduces Output Diversity
On-policy self-distillation achieves strong pass@1 accuracy by using a single model as both teacher and student, with the teacher conditioned on a correct demonstration to provide dense token-level feedback. We show that…
Reinforcement LearningSelf-Distillation Policy Optimization via Visual Feedback: Bridging Code and Visual Artifacts
Code-generating large language models (LLMs) increasingly produce visual artifacts such as charts, web pages, and slides by writing programs that are executed by non-differentiable renderers, committing to code before ob…
Neuron-Aware Data Selection for Annotation-Free LLM Self-Distillation
Post-training large language models (LLMs) without real-world interaction feedback or human-labeled supervision remains challenging, particularly in specialized domains where expert annotations are costly to obtain. Rece…
Domain GeneralizationOffline RL