Aligning LLMs Toward Multi-Turn Conversational Outcomes Using Iterative PPO

2025-11-26

AI Summary

In multi-turn goal-oriented dialogues (e.g., AI marketing agents), sparse long-horizon rewards impede stable policy optimization. Method: We propose Iterative PPO, which decomposes multi-turn RL into a sequence of single-turn RLHF subproblems. Theoretically, we prove that standard PPO updates—when using a learned multi-turn Q-function as the reward model—are equivalent to multi-turn policy improvement. Our approach integrates offline trajectory fitting with online iterative optimization, reusing existing single-turn RLHF tooling while decoupling Q-function modeling, policy training, and deployment. Contribution/Results: The framework ensures training stability while enabling online adaptation, significantly reducing development and optimization complexity for long-horizon, sparse-reward dialogue systems. It establishes a scalable, practical paradigm for aligning task-oriented LLMs with human preferences.

Technology Category

• Multiagent Systems: Multiagent Learning
• Search and Optimization: Learning to Search
• Machine Learning: Online Learning & Bandits

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Optimizing large language models (LLMs) for multi-turn conversational outcomes remains a significant challenge, especially in goal-oriented settings like AI marketing or sales agents who facilitate transactions via messaging platforms. The difficulty stems from sparse, long-horizon rewards and the discrepancy between response-level planning and token-level generation. In this technical note, we propose a formal reduction of the multi-turn RL problem into a sequence of single-turn RLHF-style problems. This is achieved by setting a learned multi-turn Q-function as the reward model for the single-turn problem. We demonstrate and prove a key insight: solving this single-turn RL problem with standard token-level PPO is equivalent to a policy improvement step within the multi-turn problem. This insight naturally leads to Iterative PPO, a batch online policy iteration algorithm that alternates between fitting Q-functions from logged conversation trajectories and improving the policy. A major practical advantage is that Iterative PPO directly leverages stable, off-the-shelf single-turn RLHF tools, making it straightforward to implement. Our method occupies a middle ground between fully online and fully offline approaches, retaining the adaptability of online updates while gaining the stability benefits of offline training.

Problem

• Optimizes LLMs for multi-turn conversational outcomes
• Addresses sparse rewards and planning-generation discrepancy
• Proposes Iterative PPO using single-turn RLHF tools

Innovation

• Iterative PPO algorithm for multi-turn conversations
• Learned Q-function as reward model for single-turn RL
• Combines online adaptability with offline training stability