When Terminal-Agent Training Stalls: Demystifying Data Generation and Verification Challenge

A new arXiv paper (2610.02405v1) reports that using a frontier model such as Claude Opus as a meta-agent to generate terminal tasks and verifiers for RL training can produce unfaithful pipelines despite runnable Docker images and executable test suites, identifying three failure classes: benchmark invalidity, harness brittleness, and reward misalignment. Prompt redesign and context extension raised baseline solvability 5.6 times, but a 9B model saturated at 81.3% mean pass@2 within 20 steps on Claude Opus-generated tasks, and adding hard tasks cut mean pass@2 to 20.6% without any change to the training configuration, which the authors cite as strong evidence that the solvability band is model-specific. The authors conclude that meta-agent reliability requires solvability-band calibration, verifier audits, and infrastructure error accounting as first-class evaluation criteria rather than post-hoc diagnostics.