Proximity-Based Multi-Turn Optimization: Practical Credit Assignment for LLM Agent Training
arXiv:2602.19225v1 Announce Type: new 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
arXiv:2602.19225v1 Announce Type: new 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 soft aggregation derives baselines through continuous semantic weighting at the step level. Extensive evaluations on ALFWorld and WebShop benchmarks demonstrate that ProxMO yields substantial performance gains over existing baselines with negligible computational cost. Ablation studies further validate the independent and synergistic efficacy of both mechanisms. Crucially, ProxMO offers plug-and-play compatibility with standard GRPO frameworks, facilitating immediate, low-friction adoption in existing industrial training pipelines. Our implementation is available at: \href{https://anonymous.4open.science/r/proxmo-B7E7/README.md}{https://anonymous.4open.science/r/proxmo}.
Executive Summary
This article introduces ProxMO, a novel framework for optimizing multi-turn LLM agents in real-world scenarios. ProxMO addresses the issue of misallocating credit in group-based policy optimization methods by integrating global context through two lightweight mechanisms: success-rate-aware modulation and proximity-based soft aggregation. The framework demonstrates substantial performance gains over existing baselines with negligible computational cost. ProxMO offers plug-and-play compatibility with standard GRPO frameworks, facilitating easy adoption in industrial training pipelines. The authors' implementation is available for public access. This breakthrough has significant implications for the development of intelligent agents in customer service automation, e-commerce assistance, and interactive task management.
Key Points
- ▸ ProxMO is a practical and robust framework for optimizing multi-turn LLM agents in real-world scenarios.
- ▸ The framework integrates global context through two lightweight mechanisms: success-rate-aware modulation and proximity-based soft aggregation.
- ▸ ProxMO demonstrates substantial performance gains over existing baselines with negligible computational cost.
Merits
Strength
ProxMO's ability to adapt to varying task difficulty and stochastic noise is a significant improvement over existing group-based policy optimization methods.
Comprehensive Evaluation
The authors provide extensive evaluations on ALFWorld and WebShop benchmarks, demonstrating the effectiveness of ProxMO in real-world scenarios.
Plug-and-Play Compatibility
ProxMO's compatibility with standard GRPO frameworks facilitates easy adoption in industrial training pipelines, making it a practical solution for widespread implementation.
Demerits
Limitation
The framework's performance gains may be limited to specific scenarios and tasks, requiring further investigation to ensure generalizability.
Dependence on Benchmark Data
The effectiveness of ProxMO relies on the quality and relevance of the benchmark data used in the evaluations, which may not be representative of real-world scenarios.
Expert Commentary
The introduction of ProxMO is a significant breakthrough in the development of multi-turn LLM agents. The framework's ability to integrate global context and adapt to varying task difficulty is a substantial improvement over existing group-based policy optimization methods. The authors' comprehensive evaluation and plug-and-play compatibility make ProxMO a practical solution for widespread implementation. However, further investigation is required to ensure generalizability and to address potential limitations. This research has significant implications for the development of intelligent agents in real-world applications, particularly in areas such as customer service automation and e-commerce assistance.
Recommendations
- ✓ Further investigation is required to ensure the generalizability of ProxMO's performance gains and to address potential limitations.
- ✓ The authors should explore the application of ProxMO in other real-world scenarios and tasks to demonstrate its versatility and effectiveness.