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This research investigates whether embodied coding agents optimize for the actual instructed task or merely overfit to specific benchmark verification criteria when exposed to them. The study demonstrates that while such exposure boosts scores on fixed tests, it leads to poor skill transfer and fails when criteria change slightly, suggesting a need for instruction-faithful evaluation metrics.
Learning the Task or Learning the Verifier? Rethinking Criteria Exposure in Embodied Self-Improvement
arXiv:2611.08318v1 Announce Type: new Abstract: Self-improving embodied agents are increasingly being developed and compared on robot simulation benchmarks. Several recent works expose the benchmark's success criteria to the learning agent, a practice we call Criteria Exposure (CE). These criteria, originally written to verify policies trained by imitation learning, often demand more than the corresponding language instructions, so a score from such a verifier may not faithfully reflect whether the instructed task was completed, especially for robot-control programs written by an agent. As embodied coding agents report ever higher benchmark scores, a natural question arises: when a coding agent is exposed to the benchmark's criteria, does it generate code that better performs the instructed task, or code that simply overfits to the verification criteria? We conduct controlled experiments with a self-improving agent with and without CE, and find that with CE the agent quickly produces code with a higher success rate under the given criteria, but this advantage disappears when the criteria are changed slightly without departing from the original instructions. More importantly, the skills the agent develops under CE transfer poorly across tasks. We further propose instruction-faithful criteria and show that they better reflect the performance of agent-generated programs.
Title:Learning the Task or Learning the Verifier? Rethinking Criteria Exposure in Embodied Self-Improvement
Abstract:Self-improving embodied agents are increasingly being developed and compared on robot simulation benchmarks. Several recent works expose the benchmark's success criteria to the learning agent, a practice we call Criteria Exposure (CE). These criteria, originally written to verify policies trained by imitation learning, often demand more than the corresponding language instructions, so a score from such a verifier may not faithfully reflect whether the instructed task was completed, especially for robot-control programs written by an agent. As embodied coding agents report ever higher benchmark scores, a natural question arises: when a coding agent is exposed to the benchmark's criteria, does it generate code that better performs the instructed task, or code that simply overfits to the verification criteria? We conduct controlled experiments with a self-improving agent with and without CE, and find that with CE the agent quickly produces code with a higher success rate under the given criteria, but this advantage disappears when the criteria are changed slightly without departing from the original instructions. More importantly, the skills the agent develops under CE transfer poorly across tasks. We further propose instruction-faithful criteria and show that they better reflect the performance of agent-generated programs.
22 pages, 8 figures, 10 tables. Project page: this https URL
Artificial Intelligence (cs.AI); Robotics (cs.RO)
arXiv:2611.08318 [cs.AI]
(or arXiv:2611.08318v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2611.08318 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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