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Reinforcement Learning Poised to Revolutionize Large Language Models for Reasoning

A comprehensive survey published on September 11, 2025, by researchers from Tsinghua University and Shanghai AI Laboratory, details the burgeoning field of Reinforcement Learning (RL) applied to Large Language Models (LLMs), aiming to transform them into sophisticated “Large Reasoning Models” (LRMs). The paper highlights how RL is not merely for aligning LLM behavior but is increasingly being used to incentivize and enhance their core reasoning capabilities, particularly in complex tasks like mathematics and coding.

The survey, titled “A Survey of Reinforcement Learning for Large Reasoning Models,” outlines the foundational components of RL for LRMs, including reward design, policy optimization, and sampling strategies. It delves into current challenges, such as computational resource demands, algorithm design, and the necessity of robust training data and infrastructure. The researchers argue that a systematic re-evaluation of the field is timely to address these challenges and pave the way for Artificial SuperIntelligence (ASI).

A key takeaway from the paper is the emergence of Reinforcement Learning with Verifiable Rewards (RLVR). Milestones like OpenAI’s “o1” and DeepSeek-R1 exemplify this trend, demonstrating that RL can effectively train LLMs to perform complex, long-form reasoning, including planning and self-correction. For instance, RLVR utilizes verifiable rewards like the correctness of a mathematical answer or the success rate of code unit tests. DeepSeek-R1, a notable open-source model, achieved strong performance by employing explicit, rule-based rewards for mathematical tasks and compiler/test-based rewards for coding. This approach signifies a new scaling axis for LLMs, where “thinking time” at inference, akin to an LLM’s internal deliberation process, can be optimized.

The paper categorizes RL methodologies for LLMs into several key areas:

  • Foundational Components: This includes various reward design strategies (verifiable, generative, dense, unsupervised), policy optimization techniques (critic-based, critic-free), and sampling strategies.
  • Foundational Problems: The survey discusses critical debates such as the role of RL in “sharpening” existing capabilities versus “discovering” new ones, the trade-offs between RL and Supervised Fine-Tuning (SFT) for generalization versus memorization, and the impact of model priors and training recipes.
  • Training Resources: The importance of static corpora, dynamic environments, and robust RL infrastructure for scalable LLM training is emphasized.
  • Applications: The paper showcases RL’s impact across diverse domains, including coding tasks, agentic behaviors, multimodal reasoning, robotics, and even medical applications. For example, in coding, RL has advanced models to perform tasks that involve complex code generation and automated software engineering.

The research team emphasizes that while RL is proving instrumental in enhancing LLMs’ reasoning abilities, challenges related to computational cost, algorithmic innovation, and data curation remain. The future of RL for LRMs lies in developing more efficient, scalable, and adaptive systems, potentially through novel RL algorithms, hybrid training approaches, and better integration of LLMs with dynamic environments. The survey concludes by highlighting promising future directions, including continual RL, memory-based RL, and the co-design of model architectures and RL algorithms, all aiming to unlock the full potential of these models for advanced reasoning and ultimately, Artificial SuperIntelligence.