Implicit Turn-Wise Policy Optimization for Proactive User-LLM Interaction
arXiv:2603.23550v1 Announce Type: new Abstract: Multi-turn human-AI collaboration is fundamental to deploying interactive services such as adaptive tutoring, conversational recommendation, and professional consultation. However, optimizing these interactions via reinforcement learning is hindered by the sparsity of verifiable intermediate rewards and the high stochasticity of user responses. To address these challenges, we introduce Implicit Turn-wise Policy Optimization (ITPO). ITPO leverages an implicit process reward model to derive fine-grained, turn-wise process rewards from sparse outcome signals. Unlike volatile token-level rewards, these turn-level signals exhibit superior robustness and may utilize a normalization mechanism to further enhance training stability. We evaluate ITPO across three representative multi-turn collaborative tasks: math tutoring, document writing, and medical recommendation. Empirical results demonstrate that ITPO, when combined with PPO, GRPO, or RLOO,
arXiv:2603.23550v1 Announce Type: new Abstract: Multi-turn human-AI collaboration is fundamental to deploying interactive services such as adaptive tutoring, conversational recommendation, and professional consultation. However, optimizing these interactions via reinforcement learning is hindered by the sparsity of verifiable intermediate rewards and the high stochasticity of user responses. To address these challenges, we introduce Implicit Turn-wise Policy Optimization (ITPO). ITPO leverages an implicit process reward model to derive fine-grained, turn-wise process rewards from sparse outcome signals. Unlike volatile token-level rewards, these turn-level signals exhibit superior robustness and may utilize a normalization mechanism to further enhance training stability. We evaluate ITPO across three representative multi-turn collaborative tasks: math tutoring, document writing, and medical recommendation. Empirical results demonstrate that ITPO, when combined with PPO, GRPO, or RLOO, consistently achieves improved convergence than existing baselines. Elaborate trajectory analysis confirms that ITPO infers turn-wise preferences that are semantically aligned with human judgment. Code is publicly available at https://github.com/Graph-COM/ITPO.
Executive Summary
This article introduces Implicit Turn-wise Policy Optimization (ITPO), a novel approach to optimizing multi-turn human-AI collaboration through reinforcement learning. ITPO addresses the challenges of sparsity and stochasticity in user responses by leveraging an implicit process reward model to derive fine-grained turn-wise process rewards. The authors evaluate ITPO across three representative tasks, demonstrating improved convergence compared to existing baselines when combined with PPO, GRPO, or RLOO. Additionally, trajectory analysis confirms that ITPO infers turn-wise preferences aligned with human judgment. While the results are promising, further work is needed to explore the generalizability of ITPO across diverse tasks and environments.
Key Points
- ▸ ITPO addresses the challenges of sparsity and stochasticity in user responses through an implicit process reward model
- ▸ ITPO achieves improved convergence compared to existing baselines in multi-turn collaborative tasks
- ▸ Trajectory analysis confirms that ITPO infers turn-wise preferences aligned with human judgment
Merits
Strength in Addressing Sparsity and Stochasticity
ITPO's implicit process reward model effectively addresses the challenges of sparsity and stochasticity in user responses, enabling fine-grained turn-wise process rewards.
Improved Convergence
ITPO achieves improved convergence compared to existing baselines in multi-turn collaborative tasks, demonstrating its potential for real-world applications.
Demerits
Limited Generalizability
The authors' evaluation of ITPO is limited to three representative tasks, and further work is needed to explore its generalizability across diverse tasks and environments.
Dependence on Specific Reinforcement Learning Algorithms
ITPO's performance is contingent upon the specific reinforcement learning algorithms used, such as PPO, GRPO, or RLOO, which may limit its applicability in certain contexts.
Expert Commentary
While ITPO demonstrates promising results in optimizing multi-turn human-AI collaboration, its limitations in generalizability and dependence on specific reinforcement learning algorithms highlight the need for further research. Nevertheless, the authors' innovative approach to addressing sparsity and stochasticity in user responses is a significant contribution to the field. As the field of human-AI interaction continues to evolve, it is essential to explore the potential applications and implications of ITPO in various contexts.
Recommendations
- ✓ Further evaluation of ITPO across diverse tasks and environments to assess its generalizability
- ✓ Investigation of ITPO's performance with different reinforcement learning algorithms to explore its applicability in various contexts
Sources
Original: arXiv - cs.LG
