Self-Challenging Language Model Agents

2025-06-02

AI Summary

Training tool-augmented large language model (LLM) agents typically relies heavily on manually curated tasks, tools, and evaluation criteria—limiting scalability and generalizability. Method: This paper proposes the Self-Challenge framework, wherein an agent assumes dual roles—challenger and executor—to autonomously generate high-quality Code-as-Task instances, each comprising a natural-language instruction, a programmatic verification function, and positive/negative test cases. Task generation is driven by tool interaction, validation is fully automated, and quality filtering ensures robustness—all without human annotation. The framework integrates reinforcement learning fine-tuning to further enhance generalization. Contribution/Results: Evaluated on M3ToolEval and TauBench, Llama-3.1-8B-Instruct trained solely on self-generated data achieves over 2× performance gain, establishing the first fully human-annotation-free paradigm for efficient, scalable tool-use agent training.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Large language models are quickly becoming the foundation for intelligent agents that are capable of using tools. However, training such agents is challenging because it requires human creation and annotation of a diverse set of tasks, tools, and evaluation criteria. In this paper, we propose the Self-Challenging framework for training an agent on high-quality tasks that are generated by itself. The agent first plays the role of challenger and generates a task after interacting with the given tools. The tasks take the form of a novel general class of problems termed Code-as-Task, which are defined by an instruction, a verification function and solution and failure cases which serve as tests, allowing to filter only for high-quality tasks. The agent then takes an executor role and trains on those tasks with reinforcement learning using the evaluation feedback as a reward. Evaluation on two existing multi-turn tool-use agent benchmarks, M3ToolEval and TauBench, shows the Self-Challenging framework achieves over a two-fold improvement in Llama-3.1-8B-Instruct, despite using only self-generated training data.

Problem

• Training intelligent agents requires diverse human-created tasks and tools
• Proposing Self-Challenging framework for agent self-generated high-quality tasks
• Improving tool-use agent performance with self-generated training data

Innovation

• Self-generated high-quality tasks training
• Code-as-Task problem definition
• Reinforcement learning with feedback