offline-learning

OGPO 框架通过语义关键状态重标注和目标条件过程奖励，将视觉-语言-动作模型的后训练从轨迹级模仿转向关键状态可达性学习，从而提升机器人操作策略在偏离演示轨迹时的恢复能力。实验在 LIBERO 和 MetaWorld 上超越标准监督微调，并在 LIBERO-Plus 及新提出的 LIBERO-ReAct 基准中展现出更强的分布偏移鲁棒性。

OpenHands researchers introduce SkillRefiner, an offline method that automatically refines agent skills from existing execution traces, rather than requiring new rollouts.

AgentBrew introduces an offline training framework for tool-use agents that learns from raw interaction trajectories without task verifiers, using retrospective task inference and PMI-based credit assignment to improve performance on real-world applications like GitHub and Notion.

PathBridger proposes a hierarchical offline goal-conditioned reinforcement learning method that explicitly connects subgoal selection with short-horizon execution through inverse dynamics modeling, achieving strong performance on benchmark tasks.

This paper introduces a method for self-evolution of open-ended dialogue skills using future-feedback prediction, converting conversational feedback into a fixed offline objective to enable reproducible skill optimization without live traffic. The approach achieves over 75% prediction accuracy on a privacy-preserving sales-assistant dataset.

EmoDistill is an offline framework that distills emotional negotiation skills into language model agents using Implicit Q-Learning for emotion selection and LoRA-based supervised fine-tuning and judge policy optimization for emotion expression, achieving higher utility in adversarial negotiations.

Auto-Dreamer introduces a learned offline memory consolidation method for language agents, decoupling fast memory acquisition from slow cross-session consolidation, and achieving higher performance with smaller memory banks, generalizing to unseen environments.

An open-source desktop tool called udemy-downloader-gui has been released, allowing users to download any Udemy course for free offline use with a single click.