@SOURADIPCHAKR18: Typical RL algorithms and on-policy distillation methods are blind samplers: they use privileged info to score rollouts…
Summary
This work proposes using privileged information to actively sample rollouts in reinforcement learning, improving on typical blind sampling methods.
View Cached Full Text
Cached at: 05/15/26, 12:45 AM
🚨Typical RL algorithms and on-policy distillation methods are blind samplers: they use privileged info to score rollouts, but not to find them.
We ask: can we use privileged info to actively sample the rollouts RL wishes it can stumble upon with compute?
⤵️ Pedagogical RL https://t.co/c6BcLBDIVv
Similar Articles
@lateinteraction: ICYMI: read the blog on Pedagogical RL Instead of sampling blindly from your LLM, leverage the label used for RLVR! Lea…
Introduces Pedagogical RL, a method that leverages privileged information to guide the sampling of successful trajectories for LLM reasoning, achieving up to 40% relative gains over GRPO and on-policy distillation.
@SOURADIPCHAKR18: We describe early experiments on *pedagogical RL*: A bitter-lesson-pilled paradigm of *training* privileged self-teache…
Introduces pedagogical RL, a paradigm where privileged self-teachers are trained to generate correct and easy-to-follow rollouts, showing it is a relatively easy RL problem.
@NielsRogge: One of the hottest terms in AI right now is "On-policy distillation". It is a post-training technique in which a studen…
On-policy distillation is highlighted as a hot post-training technique combining distillation with online RL, now listed on PapersWithCode with 183 citing papers.
Self-Distilled Policy Gradient
SDPG (Self-Distilled Policy Gradient) is a new RL training framework for LLMs that combines group-relative verifier advantages with on-policy self-distillation and KL regularization to address sparse rewards and instability in RLVR training. The method uses a shared model as both student and teacher by conditioning on privileged context, showing improved stability and performance over RLVR and self-distillation baselines.
@dbreunig: Great teachers craft demonstrations their students could have built themselves.
A tweet from Souradip Chakraborty proposes using privileged information to actively sample rollouts in reinforcement learning, contrasting with traditional blind sampling methods. The tweet is prefaced by a quote about great teachers crafting demonstrations that students could build themselves.