Self-Challenging Language Model Agents
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.
6840541af682212cae6d3548
Yifei Zhou
6840541af682212cae6d3549
Sergey Levine
6840541af682212cae6d354a
Jason Weston
6840541af682212cae6d354b
Xian Li
6840541af682212cae6d354c
Sainbayar Sukhbaatar
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2025-06-05T07:27:43.393Z
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Sainbayar Sukhbaatar
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Self-Challenging Language Model Agents
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.
The Self-Challenging framework trains intelligent agents using self-generated tasks defined as Code-as-Task, improving performance in multi-turn tool-use benchmarks.
Self-Challenging framework Code-as-Task reinforcement learning M3ToolEval TauBench Llama-3.1-8B-Instruct