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