统一玩家框架提升工具集成推理能力
UnifiedPlayers: Enhance Tool-Integrated Reasoning in Agentic Reinforcement Learning
朋友，这篇论文介绍了一个叫UnifiedPlayers的新框架，它让AI agent能更好地用工具做推理，效果比之前的方法好不少，还提到验证器的准确率很高。
这篇论文提出了一种名为UnifiedPlayers的框架，它通过规划、执行和评估三个协同的玩家来提升工具集成的推理能力。
在数学推理和通用推理任务上，该框架比现有方法高出至少3.5%和3.
9%。此外，其学习到的验证器在对抗检测方面准确率达到84.
2%，奖励信号的区分度也显著更高。
UnifiedPlayers: Enhance Tool-Integrated Reasoning in Agentic Reinforcement Learning
Self-evolving methods reduce the need for human-annotated trajectories by allowing tool-using agents to generate their own training data. Yet existing methods typically separate trajectory generation from evaluation, relying on static verifiers that cannot adapt to emerging failure modes or self-consistency signals that may reinforce errors shared across trajectories. Jointly adapting planning, execution, and evaluation offers a promising alternative, but introduces a fundamental coordination challenge: each component continuously changes the data or feedback used to train the others. We address this challenge with \textbf{UnifiedPlayers}, a cooperative framework comprising a Planning Player that generates tasks, an Execution Player that produces multi-turn trajectories with Python tool calls, and an Evaluation Player that constructs executable verifiers. We design role-specific rewards that coordinate the three players toward a shared learning objective under GRPO. Across two model backbones and twelve reasoning benchmarks, UnifiedPlayers outperforms the strongest prior baseline by at least 3.5\% on mathematical reasoning and 3.9\% on general reasoning tasks. Moreover, the learned verifier achieves 84.2\% adversarial detection accuracy, while its reward signal exhibits 2.03$\times$ higher per-question variance than a self-consistency baseline, providing more discriminative verifications. These results highlight cooperation among specialized players as a promising path toward self-enhanced tool-integrated agents.
This report is categorized under AITOP's "Papers" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include UnifiedPlayers, tool integration, reinforcement learning, reasoning models.
Source attribution: curated from arXiv cs.AI (@Wenjie Liao, Liangjie Zhao, Zehong Cao).
