← Back to all news
arXiv Machine Learning By Zeyun Deng, Jasorsi Ghosh, Fiona Xie, Kai Cheng, Yuzhe Lu, Katia Sycara, Joseph Campbell

Energy-Based Transfer for Reinforcement Learning

Read the original on arXiv Machine Learning →

The paper introduces an energy-based transfer learning approach for reinforcement learning, aiming to improve sample efficiency in continual learning scenarios. By using out-of-distribution detection, the method selectively provides teacher guidance only in states that fall within the teacher’s training distribution, preventing sub-optimal bias when tasks differ significantly. The authors theoretically connect energy scores to state-visitation patterns under on-policy RL and demonstrate empirical gains in both single-task and multi-task settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
Oct 1

On the Off-Policy Teacher in On-Policy Distillation

The paper introduces SCOUT, a co‑training framework that adapts an off‑policy teacher to better continue from student‑generated prefixes in on‑policy distillation (OPD). By periodically optimizing the teacher’s conditional continuation ability using reinforcement learning with verifiable rewards, SCOUT improves the teacher’s performance on student prefixes. Experiments across various teacher‑student setups, model scales, and reasoning domains show that SCOUT consistently enhances the effectiveness of OPD.

By Langlin Huang, Hao Liu, Mononito Goswami, Xinyu Li, Prithwith Jana, Nikos Kanakaris, Patrick Bl\"obaum, Purak Jain
reinforcement-learningefficiency
More like this →
arXiv AI
Oct 1

From Imitation to Reward Discovery: On-Policy Warmup for Agentic RL

The paper introduces On‑Policy Warmup (OPW), a teacher‑guided training stage where a student agent learns from a teacher on its own interaction trajectories before switching to reinforcement learning with verifiable rewards (RLVR). OPW differs from traditional imitation by focusing on states generated by the student’s own decisions, including imperfect actions and recovery situations. The authors provide a theoretical link between on‑policy reverse‑KL distillation and trajectory‑level distribution matching, showing that, under a competent teacher and low distillation loss, OPW can lower bound initial verifier success and reduce reward‑discovery complexity, thereby accelerating RLVR performance.

By Yitong Qiao, Tiantian He, Lei Liu, Yue Shen, Jian Wang, Jinjie Gu, Zhixuan Chu
agentsreinforcement-learningefficiency
More like this →
arXiv Machine Learning
Sep 18

Improving Online Reinforcement Learning via Bidirectional Behavior Prior Distillation

The paper introduces Bidirectional Behavior Prior Distillation (B2PD), a method that uses action‑value priors to train a conditional variational autoencoder for generating high‑value behavior support. These expert behavior priors are then distilled into the online reinforcement learning agent, reducing inefficient exploration and stabilizing policy updates. Experiments on state‑ and pixel‑based tasks show that B2PD improves sample efficiency while maintaining stable learning dynamics.

By Gong Gao, Xiao Lai, Jiaji Shen, Ning Jia, Xianhui Liu, Weidong Zhao
agentsreinforcement-learningefficiency
More like this →