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Meta-Reinforcement Learning with Self-Reflection for Agentic Search

arXiv:2603.11327v1 Announce Type: new Abstract: This paper introduces MR-Search, an in-context meta reinforcement learning (RL) formulation for agentic search with self-reflection. Instead of optimizing a policy within a single independent episode with sparse rewards, MR-Search trains a policy that conditions on past episodes and adapts its search strategy across episodes. MR-Search learns to learn a search strategy with self-reflection, allowing search agents to improve in-context exploration at test-time. Specifically, MR-Search performs cross-episode exploration by generating explicit self-reflections after each episode and leveraging them as additional context to guide subsequent attempts, thereby promoting more effective exploration during test-time. We further introduce a multi-turn RL algorithm that estimates a dense relative advantage at the turn level, enabling fine-grained credit assignment on each episode. Empirical results across various benchmarks demonstrate the advantag

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Teng Xiao, Yige Yuan, Hamish Ivison, Huaisheng Zhu, Faeze Brahman, Nathan Lambert, Pradeep Dasigi, Noah A. Smith, Hannaneh Hajishirzi · · 1 min read · 2 views
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arXiv:2603.11327v1 Announce Type: new Abstract: This paper introduces MR-Search, an in-context meta reinforcement learning (RL) formulation for agentic search with self-reflection. Instead of optimizing a policy within a single independent episode with sparse rewards, MR-Search trains a policy that conditions on past episodes and adapts its search strategy across episodes. MR-Search learns to learn a search strategy with self-reflection, allowing search agents to improve in-context exploration at test-time. Specifically, MR-Search performs cross-episode exploration by generating explicit self-reflections after each episode and leveraging them as additional context to guide subsequent attempts, thereby promoting more effective exploration during test-time. We further introduce a multi-turn RL algorithm that estimates a dense relative advantage at the turn level, enabling fine-grained credit assignment on each episode. Empirical results across various benchmarks demonstrate the advantages of MR-Search over baselines based RL, showing strong generalization and relative improvements of 9.2% to 19.3% across eight benchmarks. Our code and data are available at https://github.com/tengxiao1/MR-Search.

Executive Summary

This study proposes MR-Search, a novel meta-reinforcement learning (RL) framework that enables agentic search with self-reflection. MR-Search conditions on past episodes and adapts its search strategy across episodes, allowing agents to improve in-context exploration at test-time. By generating explicit self-reflections and leveraging them as additional context, MR-Search promotes more effective exploration during test-time. Empirical results demonstrate significant improvements over baselines, showcasing strong generalization and relative improvements of 9.2% to 19.3% across eight benchmarks. The study contributes to the development of RL algorithms and has potential applications in areas such as autonomous systems, robotics, and artificial intelligence.

Key Points

  • ▸ MR-Search is a novel meta-RL framework for agentic search with self-reflection.
  • ▸ MR-Search conditions on past episodes and adapts its search strategy across episodes.
  • ▸ The framework generates explicit self-reflections and leverages them as additional context to guide subsequent attempts.

Merits

Strength in self-reflection mechanism

MR-Search's self-reflection mechanism enables agents to reflect on past episodes and adapt their search strategy, leading to improved exploration and generalization.

Improved exploration during test-time

The framework's ability to generate explicit self-reflections and leverage them as additional context promotes more effective exploration during test-time.

Strong empirical results

The study demonstrates significant improvements over baselines, showcasing strong generalization and relative improvements of 9.2% to 19.3% across eight benchmarks.

Demerits

Limited evaluation of robustness

The study does not extensively evaluate the robustness of MR-Search in the face of variability, uncertainty, or adversarial environments.

Potential overfitting

The framework's reliance on explicit self-reflections may lead to overfitting, particularly if the training data is limited or noisy.

Expert Commentary

The study proposes a novel meta-RL framework that enables agentic search with self-reflection. MR-Search's self-reflection mechanism is a strength, enabling agents to reflect on past episodes and adapt their search strategy. However, the study could have benefited from more extensive evaluation of robustness and potential overfitting. Nevertheless, the framework's strong empirical results and potential applications make it a valuable contribution to the field. Future research should focus on exploring the robustness and generalizability of MR-Search in various environments and domains.

Recommendations

  • ✓ Future research should focus on exploring the robustness and generalizability of MR-Search in various environments and domains.
  • ✓ The development of more extensive evaluation protocols for robustness and potential overfitting would be beneficial for the field.

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