Dream-RSI turns old search traces into a training ground
The expensive part of agent self-improvement may not be the model, but deciding which searches deserve to run. Dream-RSI treats completed discovery trees as replayable simulators, letting candidate exploration policies test branching, ordering, parallelism, and stopping decisions against recorded outcomes before spending on a new rollout. The loop then deploys the best policy, adds its new search history, and repeats across algorithm design, mathematical optimization, and GPU kernels.