Google DeepMind 的 Dream-RSI：让 AI 智能体通过“做梦”回放过去的尝试来改进

Researchers at Google and Deepmind have developed a method that helps AI agents tackle difficult search tasks more efficiently. It uses past search runs to test new strategies without repeating costly computations. Self-improving AI agents are supposed to one day discover new algorithms, solutions to math problems, or faster code on their own. They follow the same basic process of proposing a solution, evaluating the result, learning from it, and trying again. Over thousands of attempts, they gradually work toward a good result. For complex tasks, the search space can grow enormous. The agent must constantly decide which promising approaches to pursue, which to try in parallel, and which to abandon. This process, called exploration, can determine whether the search succeeds or wastes compute chasing the wrong ideas. A research team from Google and Deepmind has introduced " Dream-RSI " to improve those decisions. The method changes how the agent searches, not the underlying AI model. Existing approaches generally handle exploration in two ways. A fixed search strategy can't learn from experience, so the agent may repeatedly hit the same dead ends. Adapting the strategy during a search avoids that rigidity but comes at a cost. It takes many attempts to find out whether a strategy works, and testing countless alternatives would mean repeating long, expensive runs. The researchers propose reusing data from a completed search to test alternative strategies within the space the agent has already explored. The agent records its attempts and their results as it searches, providing the data needed to replay those decisions later. The researchers compare this to finding your way through an unfamiliar area. On your first visit, you hit dead ends, double back, and struggle to find a route. Once you have a mental map, though, you can plan another route without visiting every spot again. Dream-RSI applies that principle to recorded search histories. Rather than testing a new strategy in a live run, the agent runs it against stored results. This lets it check what would have happened if it had pursued other approaches first or abandoned some earlier. The system doesn't invent entirely new solutions during replay; it tests different decisions within the recorded search tree. Because those results already exist, the agent doesn't need to generate or evaluate solutions again, avoiding the expensive computations a live run would require. That makes testing new search strategies much cheaper. The researchers call this process "dreaming." The agent plays through thousands of variations and selects the best one before putting it to work in a live search. The process repeats in a loop. After each search, the agent uses the recorded results to test better strategies, then applies the improved version to its next live run. Throughout this cycle, only the search strategy changes; the model generating the solutions remains untouched. The researchers tested Dream-RSI with Gemini 3.1 Pro and Gemini 3.7 Flash on eight tasks across three areas. Each comparison used a baseline with the same starting conditions but a fixed search strategy. One task asked the system to write the fastest possible program for a statistical calculation commonly used in genomics and finance. Dream-RSI's program ran faster than the established libraries sklearn and glmnet on all six test datasets.