Information Gain-based Policy Optimization: A Simple and Effective Approach for Multi-Turn Search Agents

📰 ArXiv cs.AI

Information Gain-based Policy Optimization is a simple and effective approach for multi-turn search agents

advanced Published 25 Mar 2026

Action Steps

Identify the limitations of outcome-based rewards in multi-turn search settings
Develop an Information Gain-based Policy Optimization approach to address reward sparsity
Implement the approach using reinforcement learning (RL) to train LLM-based agents
Evaluate the effectiveness of the approach in search-based settings

Who Needs to Know This

This approach benefits AI engineers and ML researchers working on large language model-based agents, as it enhances their ability to interact with external environments through tool use

Key Insight

💡 Information Gain-based Policy Optimization addresses reward sparsity in multi-turn search settings

Key Takeaways

Information Gain-based Policy Optimization is a simple and effective approach for multi-turn search agents

Full Article

Title: Information Gain-based Policy Optimization: A Simple and Effective Approach for Multi-Turn Search Agents

Abstract:
arXiv:2510.14967v2 Announce Type: replace-cross Abstract: Large language model (LLM)-based agents are increasingly trained with reinforcement learning (RL) to enhance their ability to interact with external environments through tool use, particularly in search-based settings that require multi-turn reasoning and knowledge acquisition. However, existing approaches typically rely on outcome-based rewards that are only provided exclusively upon generating the final answer. This reward sparsity beco

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