Flex-RSI: Benchmarking Recursive Self-Improvement of Frontier Agents
Overview
This research introduces Flex-RSI, a benchmark for evaluating recursive self-improvement in frontier agents performing vision and robotics tasks within a fixed budget. The study demonstrates that editing the agent's harness contributes to performance gains, while recording only success or failure hinders open-weight models. Results indicate that open-weight models struggle most when outputs require strict checks of physical stability or exact correctness.
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- arXiv Robotics — research abstractsSignalFlex-RSI: Benchmarking Recursive Self-Improvement of Frontier Agents
This research introduces Flex-RSI, a benchmark for evaluating recursive self-improvement in frontier agents performing vision and robotics tasks within a fixed budget. The study demonstrates that editing the agent's harness contributes to performance gains, while recording only success or failure hinders open-weight models. Results indicate that open-weight models struggle most when outputs require strict checks of physical stability or exact correctness.
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