Dialogue Model Optimization via Agent Game and Adaptive Tree-based GRPO

• URL: http://arxiv.org/abs/2602.08533v2
• Date: Tue, 10 Feb 2026 13:34:47 GMT
• Title: Dialogue Model Optimization via Agent Game and Adaptive Tree-based GRPO
• Authors: Kun Peng, Conghui Tan, Yu Liu, Guohua Tang, Zhongqian Sun, Wei Yang, Zining Zhu, Lei Jiang, Yanbing Liu, Hao Peng,
• Abstract summary: Open-ended dialogue agents aim to deliver engaging, personalized interactions by adapting to users' traits.<n>We propose a novel long-horizon framework integrating online personalization with Adaptive Tree-based Group Relative Policy Optimization.
• Score: 19.784541601653128
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open-ended dialogue agents aim to deliver engaging, personalized interactions by adapting to users' traits, but existing methods face critical limitations: over-reliance on pre-collected user data, and short-horizon biases in reinforcement learning (RL) that neglect long-term dialogue value. To address these, we propose a novel long-horizon RL framework integrating online personalization with Adaptive Tree-based Group Relative Policy Optimization (AT-GRPO). Adopting a two-agent game paradigm, a user agent constructs dynamic environments via style mimicry (learning user-specific conversational traits) and active termination (predicting turn-level termination probabilities as immediate rewards), forming an iterative cycle that drives the dialogue agent to deepen interest exploration. AT-GRPO reinterprets dialogue trajectories as trees and introduces adaptive observation ranges. Unlike full tree expansion that incurs exponential overhead, it limits each node to aggregate rewards from a stage-aware range: larger ranges support early-stage topic exploration, while smaller ranges facilitate late-stage dialogue maintenance. This design reduces rollout budgets from exponential to polynomial in the dialogue length, while preserving long-term reward capture. Extensive experiments show our framework's superior performance, sample efficiency, and robustness.

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