Google의 새로운 AI 훈련 방법은 소규모 모델이 복잡한 추론을 처리하는 데 도움이 됩니다.

Researchers at Google Cloud and UCLA have proposed a new reinforcement learning framework that significantly improves the ability of language models to learn very challenging multi-step reasoning tasks. Supervised Reinforcement Learning (SRL) reformulates problem-solving as a sequence of logical “actions,” providing rich learning signals during the training process.This approach enables smaller models to learn complex problems that were previously out of reach for other common training techniques. Experiments show that SRL not only excels on math reasoning benchmarks but also generalizes effectively to agentic software engineering tasks.SRL is a versatile training framework that can elevate smaller and less expensive models to higher reasoning abilities.The limits of current LLM reasoning trainingRecent advances in training large language models (LLMs) for reasoning have largely been driven by reinforcement learning with verifiable rewards (RLVR), a method where a model is rewarded bas