A paper titled "Function-Space Temporal Self-Distillation for Policy Optimization" proposes a reinforcement learning method for language model reasoning that replaces coarse scalar-reward supervision with denser token-level guidance from a feedback-informed self-teacher compared against an unassisted policy. The work targets Reinforcement Learning with Verifiable Rewards (RLVR), which the authors note improves reasoning but provides only coarse supervision over response trajectories. The source excerpt does not name the authors or institution behind the paper.
Reinforcement Learning with Verifiable Rewards (RLVR) improves reasoning in language models, but scalar outcomes provide coarse supervision over response trajectories. Self-distillation offers denser token-level guidance by comparing an unassisted policy with a feedback-informed self-teacher, yet th
EU-based hosting, git-push deploys, automatic HTTPS, no cold starts. Free tier with a custom domain — perfect for shipping the agent you just read about.