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Survey of Reinforcement Learning for Large Reasoning Models
This September 2025 paper provides a comprehensive overview of Reinforcement Learning (RL) as applied to Large Reasoning Models (LRMs). It breaks down the field into foundational components such as reward design and policy optimization, explaining various algorithms like PPO and GRPO. The document also discusses training resources, distinguishing between static corpora and dynamic environments, and highlights diverse applications of RL in LRMs, including coding, agentic tasks, and multimodal understanding, with a focus on models from 2025. Ultimately, the paper aims to identify future directions for scaling RL in LRMs towards achieving Artificial Superintelligence (ASI).
Source:
https://arxiv.org/pdf/2509.08827
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By mcgrofThis September 2025 paper provides a comprehensive overview of Reinforcement Learning (RL) as applied to Large Reasoning Models (LRMs). It breaks down the field into foundational components such as reward design and policy optimization, explaining various algorithms like PPO and GRPO. The document also discusses training resources, distinguishing between static corpora and dynamic environments, and highlights diverse applications of RL in LRMs, including coding, agentic tasks, and multimodal understanding, with a focus on models from 2025. Ultimately, the paper aims to identify future directions for scaling RL in LRMs towards achieving Artificial Superintelligence (ASI).
Source:
https://arxiv.org/pdf/2509.08827