4 papers indexed here
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
Recursive self-improvement requires carrying accepted changes into later improvement cycles, while studying agent-proposed changes also requires substantial research infrastructure. Existing settings often leave agents to rebuild routine infrastructure or restrict exploration to individual components. We introduce RSIG...
Recursive self-improvement (RSI) aims to achieve compounding gains by having models improve themselves. While most existing RSI systems optimize external agent harnesses or prompts around a frozen base model, data-centric RSI directly updates the model's own parameters by training on agent-generated data. However, beca...
Experiments show that Reflective On-policy Self-Distillation (ROSD), which distills transferable reasoning insights rather than complete reference trajectories, consistently outperforms standard OPSD and reinforcement learning baselines, better preserves reasoning diversity, stabilizes training, and mitigates late-stag...
It is suggested that current agents can make useful data-centric discoveries but cannot yet translate feedback into consistent improvements, and RSIBench-Data provides a measurable, auditable testbed for the research capabilities required for recursive self-improvement.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
