TR-ICRL: Test-Time Rethinking for In-Context Reinforcement Learning
arXiv:2604.00438v1 Announce Type: new Abstract: In-Context Reinforcement Learning (ICRL) enables Large Language Models (LLMs) to learn online from external rewards directly within the context window. However, a central challenge in ICRL is reward estimation, as models typically lack access to ground-truths during inference. To address this limitation, we propose Test-Time Rethinking for In-Context Reinforcement Learning (TR-ICRL), a novel ICRL framework designed for both reasoning and knowledge-intensive tasks. TR-ICRL operates by first retrieving the most relevant instances from an unlabeled evaluation set for a given query. During each ICRL iteration, LLM generates a set of candidate answers for every retrieved instance. Next, a pseudo-label is derived from this set through majority voting. This label then serves as a proxy to give reward messages and generate formative feedbacks, guiding LLM through iterative refinement. In the end, this synthesized contextual information is integr
arXiv:2604.00438v1 Announce Type: new Abstract: In-Context Reinforcement Learning (ICRL) enables Large Language Models (LLMs) to learn online from external rewards directly within the context window. However, a central challenge in ICRL is reward estimation, as models typically lack access to ground-truths during inference. To address this limitation, we propose Test-Time Rethinking for In-Context Reinforcement Learning (TR-ICRL), a novel ICRL framework designed for both reasoning and knowledge-intensive tasks. TR-ICRL operates by first retrieving the most relevant instances from an unlabeled evaluation set for a given query. During each ICRL iteration, LLM generates a set of candidate answers for every retrieved instance. Next, a pseudo-label is derived from this set through majority voting. This label then serves as a proxy to give reward messages and generate formative feedbacks, guiding LLM through iterative refinement. In the end, this synthesized contextual information is integrated with the original query to form a comprehensive prompt, with the answer determining through a final round of majority voting. TR-ICRL is evaluated on mainstream reasoning and knowledge-intensive tasks, where it demonstrates significant performance gains. Remarkably, TR-ICRL improves Qwen2.5-7B by 21.23% on average on MedQA and even 137.59% on AIME2024. Extensive ablation studies and analyses further validate the effectiveness and robustness of our approach. Our code is available at https://github.com/pangpang-xuan/TR_ICRL.
Executive Summary
This article proposes Test-Time Rethinking for In-Context Reinforcement Learning (TR-ICRL), a novel framework for Large Language Models (LLMs) to learn online from external rewards within the context window. TR-ICRL addresses the challenge of reward estimation by retrieving relevant instances from an unlabeled evaluation set, generating candidate answers, and using majority voting to derive pseudo-labels. These pseudo-labels guide the LLM through iterative refinement, synthesizing contextual information with the original query. TR-ICRL is evaluated on reasoning and knowledge-intensive tasks, demonstrating significant performance gains, with improvements of 21.23% on MedQA and 137.59% on AIME2024. The article contributes to the development of more efficient and effective LLMs, with potential applications in various domains.
Key Points
- ▸ TR-ICRL is a novel framework for In-Context Reinforcement Learning that addresses the challenge of reward estimation
- ▸ The framework retrieves relevant instances from an unlabeled evaluation set and uses majority voting to derive pseudo-labels
- ▸ TR-ICRL achieves significant performance gains on reasoning and knowledge-intensive tasks, with improvements of 21.23% on MedQA and 137.59% on AIME2024
Merits
Strength in Addressing Reward Estimation
TR-ICRL effectively addresses the challenge of reward estimation in In-Context Reinforcement Learning by proposing a novel framework that retrieves relevant instances and uses majority voting to derive pseudo-labels.
Improved Performance on Reasoning and Knowledge-Intensive Tasks
TR-ICRL demonstrates significant performance gains on mainstream reasoning and knowledge-intensive tasks, with impressive improvements on MedQA and AIME2024.
Demerits
Limited Evaluation on Diverse Tasks
The article primarily evaluates TR-ICRL on reasoning and knowledge-intensive tasks, with limited consideration of its performance on other types of tasks or domains.
Lack of Discussion on Generalizability
The article does not provide a comprehensive discussion on the generalizability of TR-ICRL to different scenarios, contexts, or domains.
Expert Commentary
The article proposes a novel framework for In-Context Reinforcement Learning, TR-ICRL, which addresses the challenge of reward estimation by retrieving relevant instances from an unlabeled evaluation set and using majority voting to derive pseudo-labels. The framework demonstrates significant performance gains on reasoning and knowledge-intensive tasks, with impressive improvements on MedQA and AIME2024. While the article has several merits, including its strength in addressing reward estimation and improved performance on reasoning and knowledge-intensive tasks, it also has some limitations, including limited evaluation on diverse tasks and a lack of discussion on generalizability. The findings of this article have significant implications for the development of more efficient and effective LLMs and raise important questions about explainability in AI and the role of reinforcement learning in AI development.
Recommendations
- ✓ Future research should explore the generalizability of TR-ICRL to different scenarios, contexts, and domains.
- ✓ The authors should provide a more comprehensive discussion on the explainability of pseudo-labels in TR-ICRL and their contribution to the model's decision-making process.
Sources
Original: arXiv - cs.CL
