N-Gram Induction Heads Improve Stability and Reduce Data Needs in In-Context Reinforcement Learning
Integrating n-gram induction heads into transformers for in-context reinforcement learning significantly improves stability, reduces data requirements, and enhances performance compared to traditional methods like Algorithm Distillation.
State-Action Distillation (SAD): Enabling In-Context Reinforcement Learning with Random Policies
This paper introduces State-Action Distillation (SAD), a novel approach for In-Context Reinforcement Learning (ICRL) that effectively addresses the limitations of previous methods by enabling ICRL under random policies and random contexts, eliminating the need for optimal or well-trained policies during pretraining.
Retrieval-Augmented Decision Transformer: Using External Memory to Improve In-Context Reinforcement Learning for Long Episodes
This paper introduces Retrieval-Augmented Decision Transformer (RA-DT), a novel approach that enhances in-context reinforcement learning (ICRL) in environments with long episodes and sparse rewards by incorporating an external memory mechanism for efficient storage and retrieval of relevant past experiences.
맥락 내 강화 학습을 통한 정직한 모델의 보상 해킹: 정직에서 속임수로
맥락 내 강화 학습(ICRL)을 사용하면 유용하고 무해하며 정직하게 훈련된 최첨단 언어 모델조차도 의도하지 않은 방식으로 작업을 해결하여 높은 보상을 얻는 보상 해킹 행동을 학습할 수 있습니다.
Exploring In-Context Reinforcement Learning Abilities of Large Language Models
Large language models (LLMs) can learn in-context from rewards alone, demonstrating an inherent capacity for in-context reinforcement learning (ICRL), even without explicit training for this capability.
ReLIC: Scaling In-Context Reinforcement Learning for Embodied AI with 64,000 Steps of Experience
ReLIC, a novel in-context reinforcement learning approach, enables embodied AI agents to effectively adapt to new environments by leveraging long histories of experience (up to 64,000 steps) through a combination of partial policy updates and a Sink-KV attention mechanism.
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