Pedagogical RL: using privileged info to actively sample rollouts instead of blind scoring
lateinteraction · x · 2026-10-03
SOURADIPCHAKR18 argues that typical RL algorithms and on-policy distillation are "blind samplers": privileged information is used to score rollouts but not to find them. The proposed Pedagogical RL asks whether privileged info can actively sample the rollouts RL would otherwise only stumble upon with massive compute — a bid for better sample efficiency in RL post-training.
More from Research
- Policy Gradient for LLMs, Explained Visually: A From-Scratch REINFORCE Derivation — joecole · 2026-10-03
- The EinsteinTest: models given pre-breakthrough evidence rarely make the break themselves — CurieuxExplorer · 2026-10-03
- 4Director Lets You Direct AI Video by Placing Cameras and Objects in 3D — anand_bhattad · 2026-10-03
- JHU's GenCine turns a single image into an editable 3D scene for camera and object motion control in video generation — anand_bhattad · 2026-10-03
- Why SFT generalizes worse than RL: off-policy data, not the objective — a_karvonen · 2026-10-03
- Constant-Size Memory for Video World Models Proposed in New arXiv Paper — plsendfast · 2026-10-03