Googles new AI training method helps small models tackle complex reasoning

Researchers have introduced a novel reinforcement learning framework called Sequential Reasoning Learning (SRL), which enhances the multi-step reasoning capabilities of language models by reformulating problem-solving as a sequence of logical actions, thereby providing richer training signals. This approach allows smaller, less resource-intensive models to master complex tasks such as advanced math reasoning and software engineering, surpassing the limitations of traditional reinforcement learning with verifiable rewards (RLVR), which often struggles with the high computational costs and difficulty in learning from partial successes in multi-step problems. Unlike RLVR, where models are rewarded only upon correct final answers, SRL emphasizes