I increasingly think recursive self-improvement could challenge the current RL-centric paradigm.

RL improves a model by using rewards and gradient descent to update parameters. RSI can work at a higher level: use reasoning to explore possible solutions, critique failures, select better strategies, generate higher-quality trajectories, and then distill them back into the model through SFT or other updates.

The key difference is exploration.

Traditional RL often explores through large-scale sampling and trial and error. Reasoning can make exploration far more directed: analyze why an attempt failed, eliminate large parts of the search space, form new hypotheses, and choose the next experiment more intelligently.

This could greatly improve exploration efficiency and expand the range of problems that learning systems can handle, especially in domains where rewards are sparse, delayed, difficult to specify, or impossible to automate.

In that sense, future AI learning may look less like blind policy optimization and more like human learning:

reason → hypothesize → test → reflect → retain the useful insight.

RL may not disappear, but its role could change. Instead of being the main mechanism for discovering better strategies, it may become the mechanism that compresses and internalizes discoveries made through reasoning, search, memory, and verification.

I increasingly think recursive self-improvement could challenge the current RL-centric paradigm.

RL improves a model by using rewards and gradient descent to update parameters. RSI can work at a higher level: use reasoning to explore possible solutions, critique failures, select better strategies, generate higher-quality trajectories, and then distill them back into the model through SFT or other updates.

The key difference is exploration.

Traditional RL often explores through large-scale sampling and trial and error. Reasoning can make exploration far more directed: analyze why an attempt failed, eliminate large parts of the search space, form new hypotheses, and choose the next experiment more intelligently.

This could greatly improve exploration efficiency and expand the range of problems that learning systems can handle, especially in domains where rewards are sparse, delayed, difficult to specify, or impossible to automate.

In that sense, future AI learning may look less like blind policy optimization and more like human learning:

reason → hypothesize → test → reflect → retain the useful insight.

RL may not disappear, but its role could change. Instead of being the main mechanism for discovering better strategies, it may become the mechanism that compresses and internalizes discoveries made through reasoning, search, memory, and verification.

Source:https://twitter.com/spark_ren/status/2083785265601249558