title: Self-Challenging Language Model Agents
summary: 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.
paper_authors._id: 6840541af682212cae6d3548
paper_authors.name: Yifei Zhou
paper_authors._id: 6840541af682212cae6d3549
paper_authors.name: Sergey Levine
paper_authors._id: 6840541af682212cae6d354a
paper_authors.name: Jason Weston
paper_authors._id: 6840541af682212cae6d354b
paper_authors.name: Xian Li
paper_authors._id: 6840541af682212cae6d354c
paper_authors.name: Sainbayar Sukhbaatar
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-05T07:27:43.393Z
paper_authors._id: 66a8611eb51510d82ed54231
paper_authors.avatarUrl: /avatars/ad559e774fee4914091b82c9831ae2a2.svg
paper_authors.fullname: Sainbayar Sukhbaatar
paper_authors.type: user
paper_authors.user: sainbar
paper_title: Self-Challenging Language Model Agents
paper_summary: 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.
paper_ai_summary: The Self-Challenging framework trains intelligent agents using self-generated tasks defined as Code-as-Task, improving performance in multi-turn tool-use benchmarks.
paper_ai_keywords: Self-Challenging framework Code-as-Task reinforcement learning M3ToolEval TauBench Llama-3.1-8B-Instruct