High-level ReLearning is proposed, a novel HRL approach to address the problem of non-stationary environments at the high level since the low-level policy is constantly changing and a student network is introduced to escape the influence of past data.
This work proposes self-instructing policy optimization (SIPO) with a contrastive self-teacher to provide dense credit, and demonstrates that SIPO outperforms both RLVR and OPSD baselines without an external teacher or additional generation.
Zhenrui Yue, Hui-Min Zeng, Yue-Qi Wang et al.· 0 citations
RLTL;DR is introduced, a promising compacted training paradigm of the form"on this sort of task, keep this sort of thing in mind", which it is hoped will inspire future research on.
Michael Kirchhof, Eleonora Gualdoni, Andrew Szot et al.· 1 citation
This article proposes a novel causal approach to learning subgoal spaces that learns higher quality subgoal representations and outperforms current methods, and proposes a regularization method to alleviate representation collapse and improve stability.
This work proposes Single-rollout Autoregressive Policy Optimization (SAPO), a low-memory and compute-efficient framework in which the policy and value functions share a single autoregressive backbone, and introduces a trajectory-level generalized advantage estimator that combines lambda-returns with batch normalizatio...
Introducing new capabilities to frontier models has long been the goal of posttraining, which predominantly employs supervised finetuning (SFT) and reinforcement learning (RL) to this end. Conventional wisdom dictates that RL enables strong generalization on new tasks without losing existing capabilities, while SFT is...
This work proposes RetireOPD (Self-Retiring On-Policy Distillation), which first optimizes a decoupled, skill-conditioned teacher with environment rewards and then trains a skill-free student jointly with RL and OPD.
Yan Yu, Zhengxi Lu, Yi-Zhou Liu et al.· 1 citation
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