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[2602.19225] Proximity-Based Multi-Turn Optimization: Practical Credit Assignment for LLM Agent Training
Summary
The paper presents Proximity-Based Multi-Turn Optimization (ProxMO), a framework designed to improve credit assignment in LLM agent training, enhancing performance in real-world applications.
Why It Matters
As LLM agents become integral to various sectors, optimizing their training is crucial for efficiency and effectiveness. ProxMO addresses existing limitations in credit allocation during training, potentially leading to better performance in complex tasks, which is vital for industries relying on AI-driven solutions.
Key Takeaways
- ProxMO improves credit assignment in multi-turn LLM training.
- The framework adapts gradient intensity based on task difficulty.
- It utilizes proximity-based aggregation for better performance metrics.
- ProxMO shows significant gains over existing methods with low computational costs.
- The approach is compatible with standard GRPO frameworks for easy implementation.
Computer Science > Artificial Intelligence arXiv:2602.19225 (cs) [Submitted on 22 Feb 2026] Title:Proximity-Based Multi-Turn Optimization: Practical Credit Assignment for LLM Agent Training Authors:Yangyi Fang, Jiaye Lin, Xiaoliang Fu, Cong Qin, Haolin Shi, Chang Liu, Peilin Zhao View a PDF of the paper titled Proximity-Based Multi-Turn Optimization: Practical Credit Assignment for LLM Agent Training, by Yangyi Fang and 6 other authors View PDF HTML (experimental) Abstract:Multi-turn LLM agents are becoming pivotal to production systems, spanning customer service automation, e-commerce assistance, and interactive task management, where accurately distinguishing high-value informative signals from stochastic noise is critical for sample-efficient training. In real-world scenarios, a failure in a trivial task may reflect random instability, whereas success in a high-difficulty task signifies a genuine capability breakthrough. Yet, existing group-based policy optimization methods rigidly rely on statistical deviation within discrete batches, frequently misallocating credit when task difficulty fluctuates. To address this issue, we propose Proximity-based Multi-turn Optimization (ProxMO), a practical and robust framework engineered specifically for the constraints of real-world deployment. ProxMO integrates global context via two lightweight mechanisms: success-rate-aware modulation dynamically adapts gradient intensity based on episode-level difficulty, while proximity-based ...
