feat: port standalone trainer robustness to TRL#238
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- Add DiagnosticsCallback to trl_callbacks.py: logs loss, |loss|, grad_norm, reward in scientific notation (matches standalone trainer diagnostic output) - Register DiagnosticsCallback in trl_wrapper.py alongside TelemetryCallback - Add test_trl_robustness.py: 19 tests covering health check, corrupt screenshot retry, stuck detection, truncation warning, diagnostics callback, and empty rollout result shape Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Summary
loss,|loss|,grad_norm,rewardin scientific notation at each training step — matches the standalone trainer's diagnostic output that operators rely on for debuggingtrl_rollout.pyTest plan
🤖 Generated with Claude Code