The October 29 2025 Google research paper introduces Supervised Reinforcement Learning (SRL), a novel framework designed to improve the complex, multi-step reasoning abilities of large language models (LLMs). The core issue addressed is that conventional training methods like Supervised Fine-Tuning (SFT) and outcome-based Reinforcement Learning with Verifiable Rewards (RLVR) struggle with difficult problems because they either overfit rigid expert paths or receive only sparse, uninformative final outcome rewards. SRL overcomes this by reformulating problem-solving as a sequence of logical "actions" and providing dense, step-wise rewards based on the similarity between the model's actions and expert demonstrations. Through extensive experiments, the paper demonstrates that SRL significantly outperforms baseline methods on challenging mathematical reasoning and software engineering benchmarks, especially when used to initialize training before subsequent refinement with RLVR. Source: https://arxiv.org/pdf/2510.25992