具有目标条件强化学习的指令遵循任务自引导计划提取
Self-Guided Plan Extraction for Instruction-Following Tasks with Goal-Conditional Reinforcement Learning
AI总结：
本文提出SuperIgor框架，通过自学习机制使语言模型生成并优化高层计划，减少手动标注需求。通过迭代共训练，强化学习代理和语言模型共同改进，验证了在动态和随机环境中严格遵循指令的能力。
摘要：
我们介绍SuperIgor，一种用于指令遵循任务的框架。与依赖预定义子任务的先前方法不同，SuperIgor使语言模型通过自学习机制生成和优化高层计划，减少对人工标注数据集的依赖。我们的方法涉及迭代共训练：强化学习代理被训练以遵循生成的计划，同时语言模型根据强化学习反馈和偏好调整和修改这些计划。这创建了一个反馈循环，使代理和规划器共同改进。我们在动态丰富和随机性的环境中验证了我们的框架。结果表明，SuperIgor代理比基线方法更严格地遵循指令，同时在之前未见过的指令上表现出强大的泛化能力。
英文摘要：
We introduce SuperIgor, a framework for instruction-following tasks. Unlike prior methods that rely on predefined subtasks, SuperIgor enables a language model to generate and refine high-level plans through a self-learning mechanism, reducing the need for manual dataset annotation. Our approach involves iterative co-training: an RL agent is trained to follow the generated plans, while the language model adapts and modifies these plans based on RL feedback and preferences. This creates a feedback loop where both the agent and the planner improve jointly. We validate our framework in environments with rich dynamics and stochasticity. Results show that SuperIgor agents adhere to instructions more strictly than baseline methods, while also demonstrating strong generalization to previously unseen instructions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
