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Self-Challenging Language Model Agents
Subject Area: It falls under Computer Science, specifically Artificial Intelligence (cs.AI) and Computation and Language (cs.CL)1.
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Core Concept: The paper proposes a "Self-Challenging framework" for training intelligent agents1. This framework allows an agent to generate its own high-quality tasks1.
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Task Generation: The agent acts as a "challenger" to generate tasks after interacting with given tools. These tasks are defined as a novel class of problems called "Code-as-Task," which include an instruction, a verification function, and solution/failure cases that act as tests to filter for high-quality tasks1.
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Training: After generating tasks, the agent takes on an "executor role" and trains on these self-generated tasks using reinforcement learning, with evaluation feedback serving as a reward1.
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Performance: The Self-Challenging framework showed significant improvement (over two-fold) in Llama-3.1-8B-Instruct on two existing multi-turn tool-use agent benchmarks, M3ToolEval and TauBench, despite using only self-generated training data1.
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Availability: The paper, submitted on June 2, 2025, is available in PDF and experimental HTML formats12.
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Associated Tools/Platforms: The sources also mention various bibliographic tools, citation tools, and platforms associated with the paper, such as NASA ADS, Google Scholar, Semantic Scholar, alphaXiv, CatalyzeX Code Finder, DagsHub, Huggingface, Papers with Code, ScienceCast, Replicate, TXYZ.AI, Influence Flower, and CORE Recommender34. Some of these are part of arXivLabs, which supports experimental projects45.
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By CCStudiosSubject Area: It falls under Computer Science, specifically Artificial Intelligence (cs.AI) and Computation and Language (cs.CL)1.
•
Core Concept: The paper proposes a "Self-Challenging framework" for training intelligent agents1. This framework allows an agent to generate its own high-quality tasks1.
•
Task Generation: The agent acts as a "challenger" to generate tasks after interacting with given tools. These tasks are defined as a novel class of problems called "Code-as-Task," which include an instruction, a verification function, and solution/failure cases that act as tests to filter for high-quality tasks1.
•
Training: After generating tasks, the agent takes on an "executor role" and trains on these self-generated tasks using reinforcement learning, with evaluation feedback serving as a reward1.
•
Performance: The Self-Challenging framework showed significant improvement (over two-fold) in Llama-3.1-8B-Instruct on two existing multi-turn tool-use agent benchmarks, M3ToolEval and TauBench, despite using only self-generated training data1.
•
Availability: The paper, submitted on June 2, 2025, is available in PDF and experimental HTML formats12.
•
Associated Tools/Platforms: The sources also mention various bibliographic tools, citation tools, and platforms associated with the paper, such as NASA ADS, Google Scholar, Semantic Scholar, alphaXiv, CatalyzeX Code Finder, DagsHub, Huggingface, Papers with Code, ScienceCast, Replicate, TXYZ.AI, Influence Flower, and CORE Recommender34. Some of these are part of arXivLabs, which supports experimental projects45.