A preprint on arXiv introduces Recursive Reward Improvement via Adversarial Meta-Prompt Evolution, a method for Recursive Self-Improvement (RSI). The authors note that RSI has gained attention because it can operate with limited data. In the described setup, a proposer model generates training tasks and a solver model learns to solve them through reinforcement learning.
The title and abstract point to the core idea: rather than only improving the solver, the reward model itself is improved recursively. This is done through adversarial meta-prompt evolution, though the abstract does not give details of the evolution procedure or experimental results.
Because only the abstract was available for this digest, there are no independent results to compare. The claims should be treated as the authors' proposal until a fuller evaluation is available.