RSPO: Reward-Swap Policy Optimization for Multi-Turn LLM Agents

2026-07-06 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Advanced, quick

Summary

Reward-Swap Policy Optimization (RSPO) is a novel method designed to enhance the training of large language models (LLMs) for multi-turn interactive tasks using reinforcement learning. Traditional approaches face challenges: sparse outcome rewards lead to slow convergence and limit the discovery of successful trajectories, while dense process rewards, though faster, risk misalignment with true outcomes, degrading final performance. RSPO addresses this by employing a reward-swap mechanism that utilizes rich dense process rewards to guide training while maintaining consistency with the ground-truth outcome rewards. This approach ensures diverse sampled trajectories and aligns the optimization objective with the true task goals, thereby improving model performance. Extensive experiments on challenging agent benchmarks, WebShop and ALFWorld, demonstrate that RSPO consistently improves performance across various reinforcement learning algorithms, including GRPO, PPO, and GiGPO.

Key takeaway

For Machine Learning Engineers developing LLM agents for multi-turn interactive tasks, RSPO provides a critical solution to overcome sparse outcome rewards and potential dense reward misalignment. By integrating RSPO's reward-swap mechanism, you can achieve faster convergence and higher performance ceilings, ensuring your models learn diverse, successful trajectories. Consider adopting RSPO with algorithms like PPO or GRPO to enhance your agent's robustness on benchmarks such as WebShop or ALFWorld.

Key insights

RSPO combines dense process rewards with true outcome rewards via a reward-swap mechanism to optimize multi-turn LLM agent training.

Principles

• Sparse outcome rewards hinder multi-turn LLM training.
• Dense process rewards accelerate learning but risk misalignment.
• Reward-swap mechanisms can balance signal richness and objective consistency.

Method

RSPO employs a reward-swap mechanism to integrate dense process rewards for training guidance while ensuring the optimization objective remains aligned with true outcome rewards. This enhances trajectory diversity and performance.

In practice

• Apply RSPO to GRPO, PPO, or GiGPO for LLM agents.
• Test RSPO on multi-turn tasks like WebShop or ALFWorld.
• Use reward-swap to mitigate sparse reward issues in RL.

Topics

• Reward-Swap Policy Optimization
• Large Language Models
• Reinforcement Learning
• Multi-turn Agents
• WebShop Benchmark
• ALFWorld Benchmark
• Policy Optimization

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.