Rethinking Self-Repair in Code-as-Policy Agents: What Drives Retry Success?
Overview
This research investigates the drivers of retry success in code-as-policy agents for simulated robot manipulation. The authors compare execution feedback templates against resampling and static task guidance baselines on a dataset of 252 initial failures. Using Qwen2.5-Coder-7B, the study finds that fixed lookup feedback templates recover 85.3% of tasks within five retries, outperforming resampling (68.3%) under the same retry cap and token budgets. The research also notes that mismatched feedback can hinder performance on specific tasks like nut-insertion requiring a rim grasp, suggesting that evaluations should include diverse baselines to properly assess self-repair capabilities.
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Event evidence and corrections
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Environment: simulation
“arXiv:2611.06863v1 Announce Type: new Abstract: Code-as-policy agents often use execution feedback to retry failed programs. Additional sampling and task guidance can also improve success, leaving the contribution of failure-specific feedback unclear. We study this question in simulated robot manipulation. A fixed lookup selects feedback templates using failure labels and the API variant. A frozen code model generate”
Exact source · revision 1Source owner not reported
Reported quantity · retry attempts: 5 other · Basis not reported
“s a fresh program after each scene reset. On the same 252 initial failures, Qwen2.5-Coder-7B recovers 85.3% within five retries, compared with 68.3% for resampling with the same retry cap. The advantage persists under fixed total-token budgets. Feedback from other episodes and fixed task guidance yield similar recovery point estimates, with no detected benefit from matching the coarse failure label. In a separate stu”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalRethinking Self-Repair in Code-as-Policy Agents: What Drives Retry Success?
This research investigates the drivers of retry success in code-as-policy agents for simulated robot manipulation. The authors compare execution feedback templates against resampling and static task guidance baselines on a dataset of 252 initial failures. Using Qwen2.5-Coder-7B, the study finds that fixed lookup feedback templates recover 85.3% of tasks within five retries, outperforming resampling (68.3%) under the same retry cap and token budgets. The research also notes that mismatched feedback can hinder performance on specific tasks like nut-insertion requiring a rim grasp, suggesting that evaluations should include diverse baselines to properly assess self-repair capabilities.
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