SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning
Researchers introduce SEED, a framework that transforms completed on-policy trajectories into hindsight skills and distills these into the policy model to enhance agentic reinforcement learning. SEED provides dense token-level supervision in addition to outcome-based RL, and experiments demonstrate consistent improvements in performance and sample efficiency on both text-based and vision-based tasks. The approach also shows robust generalization to unseen scenarios.
Why it matters: SEED narrows the supervision gap in outcome-based RL by generating and distilling reusable skills, leading to improved sample efficiency and generalization for agentic tasks.
Full story at: arXiv Computation and Language ↗