When Terminal-Agent Training Stalls: Demystifying Data Generation and Verification Challenge
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Abstract (arXiv)
Using a frontier model like Claude Opus as a meta-agent to generate terminal tasks and verifiers for RL training is increasingly common. Yet a runnable Docker image and executable test suite do not guarantee a faithful end-to-end pipeline for terminal agent training. We present a meta-agent pipeline motivated by this gap, diagnosing three classes of failure: benchmark invalidity, harness brittleness, and reward misalignment. Prompt redesign and context extension raise baseline solvability 5.6 times, but a 9B model saturates at 81.3% mean pass@2 within 20 steps on Claude Opus-generated tasks. Adding hard tasks reduces mean pass@2 to 20.6% without changing the training configuration, a strong evidence that the solvability band is model-specific. These findings demonstrate that meta-agent reliability requires solvability-band calibration, verifier audits, and infrastructure error accounting as first-class evaluation criteria, not post-hoc diagnost.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 10 references that are in the map. First posted on arXiv 1 Oct 2026.
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6 of 10 references.
References · 6
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
- 2024DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligencecited by 1,181
- 2025Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoningcited by 92
- 2026Endless Terminals: Scaling RL Environments for Terminal Agentscited by 54
- 2026Tmax: A simple recipe for terminal agentscited by 33
- 2026Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Modelcited by 5
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