Dream‑RSI: automatic self‑improvement through reflection
INEMA guide explaining the Dream‑RSI concept and its link to the LOOP‑R project.
Dream-RSI is a proposal for automatic improvement through self-reflection. Instead of changing a language model or its weights, it aims to improve the exploration policy: the strategy that chooses which experiments to try. The cycle starts with experiments run by an agent, which form a history called a discovery tree. Replay reuses that history to simulate alternative strategies.
The idea is to compare exploration paths without repeating experiments that may be costly. In the Lasso scenario with Gemini-3.1-Pro described in the source, Replay reduced the number of calls from 550 to 317. The connection to LOOP-R appears in layers: the agent learns from its work, LOOP-R improves the process, and Dream-RSI seeks to improve the strategy used to find ways to improve it.
To get started, consult INEMA’s educational guide to Dream-RSI and its connection to LOOP-R. It presents the concept, figures, and a learning plan; it is not a scientific implementation of the paper. As you read, notice the difference between improving the model and improving the exploration strategy, as well as Replay’s role in using experiments already recorded.
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