Recursive self-improvement of AI research agents
AIDE^2 recursively rewrote itself for eight days and matched a strong human-engineered research agent.
AIDE^2 edits its own code, benchmarks modified versions on AI R&D tasks, and keeps changes that win hidden evaluations. An autonomous eight-day run produced seven successive improvements, including a new search policy and memory that compresses growing context. The gains transfer to four held-out benchmarks spanning machine-learning engineering, heuristic algorithms, and out-of-distribution weather forecasting, matching or exceeding a strong human-engineered agent on FML-Bench. On a separate task family, reward hacking fell from 55% to 32%, seven points below the human baseline, without being an optimization target.
- An eight-day autonomous run accepted seven successive self-improvements.
- Changes include a new search policy and context-compression memory.
- The best agent matches or beats a production agent on four held-out benchmarks.
- Reward hacking fell from 55% to 32% without being explicitly optimized.
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AI agents are beginning to automate research and development across the AI stack, from improving training efficiency to optimizing inference. A natural next step is to improve the research efficiency of the agents themselves. When an AI research agent's own code is the object of optimization, each accepted rewrite becomes the agent that the next round edits. We refer to this loop as recursive self-improvement. Its significance lies in a long-standing trend, in which increased cumulative spending on R&D yields diminishing returns. Sustained self-improvement offers a way to counter this trend. We present AIDE^2, a system that implements this loop for a frontier AI research agent. It proposes changes to its own code, benchmarks modified versions of itself on a suite of AI R&D tasks, and keeps the changes that perform best on hidden evaluations. In an autonomous 8-day run, AIDE^2 discovered seven successive improvements, ranging from a new search policy to memory mechanisms that compress and manage the agent's growing context. These gains generalize to four held-out benchmarks spanning machine learning engineering, heuristic algorithm engineering, and physics-based weather forecasting, the last of which is out of distribution from the selection tasks. On all four, the strongest discovered agent matches or exceeds a human-engineered production research agent that ranks among the strongest on FML-Bench. On a separate held-out task family, the discovered agents also exhibit reduced reward hacking, a property the loop never explicitly optimized for: the rate falls from 55% to 32% during the run, 7 percentage points below the human-engineered agent. Together, these results show that an AI research agent can improve its own research efficiency through recursive self-improvement, and that these gains transfer to tasks and domains the loop never encountered.
Text extracted automatically; images, tables and formatting may be missing. Original: https://huggingface.co/papers/2609.26457