TAO-RL improves agentic RL by filtering degenerate trajectories and reshaping advantages with tool-aware entropy bonuses, yielding better performance on reasoning benchmarks.
Et-agent: Incentivizing effective tool-integrated reasoning agent via behavior calibration,
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Survey framing LLM agents as model-plus-harness systems, decomposing harness responsibilities, mapping them to tasks, and highlighting open challenges in evaluation, safety, and co-evolution.
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Tool-Aware Optimization with Entropy Guidance for Efficient Agentic Reinforcement Learning
TAO-RL improves agentic RL by filtering degenerate trajectories and reshaping advantages with tool-aware entropy bonuses, yielding better performance on reasoning benchmarks.
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From Question Answering to Task Completion: A Survey on Agent System and Harness Design
Survey framing LLM agents as model-plus-harness systems, decomposing harness responsibilities, mapping them to tasks, and highlighting open challenges in evaluation, safety, and co-evolution.