Reinforcement learning increasingly shapes the capabilities of large-scale language models, pushing beyond simple text generation towards complex inference tasks, and new research comprehensively examines this rapidly evolving field. Kai Yang Chang, Yuxin Zuo and Bingxiang He are from Tsinghua University, who detail how reinforcement learning transforms language models into powerful inference engines, excels in areas such as mathematics and coding. Researchers identify key challenges that hinder further advancement, encompassing algorithm design, data requirements and necessary infrastructure beyond computational requirements, and the field aims to become increasingly sophisticated artificial intelligence. This review highlights opportunities and directions for future research, including fundamental components, core issues, training resources, and downstream applications, including recent advances since the release of DeepSeek-R1, and aims to completely lock the possibilities of reinforcement learning for the ability of a broader reason.
Reinforcement learning improves inference in large-scale language models
This paper explores recent advances in reinforcement learning (RL) to improve the inference capabilities of large-scale language models (LLMs). RL addresses the limitations of LLM in tasks that require continuous decision-making and complex inference when direct supervision is difficult. This study explores how RL techniques improve performance in areas such as gameplay, robot control, and dialogue systems, and how reward signals can effectively guide LLM learning and strategies to overcome challenges related to research and sample efficiency. This study details the various RL algorithms that can be adapted for use in LLM, including policy gradient methods, value-based methods, and actor and criticism architectures, and examines techniques for addressing the issue of credit allocation, where it is difficult to determine the actions that contributed to a particular outcome.
Reinforcement Learning Power Next Generation Inference Model
Recent research shows the growing impact of reinforcement learning (RL) in advancing the capabilities of large-scale language models (LLM), transforming them into large-scale inference models (LRM). Scientists have achieved important milestones, and models such as the DeepSeek-R1 match Openai's O1 series performance across a variety of benchmarks. These models employ a multi-stage training pipeline and in some cases work without monitored fine-tuning, known as “zero RL.” More unique models quickly followed, including Claude-3, 7-Sonnet, Gemini2.0 and 2.
5, Seed Thinking 1. 5, and O3 series each introduces increasingly advanced reasoning abilities. Openai has also released the most capable AI systems to date: GPT-OSS-120B and GPT-5. This dynamically switches between efficient and deeper inference models. A parallel open source initiative has further improved the benchmark score by expanding the landscape by the QWEN3 series, which includes the QWEN3-235B model, which matches the performance of the QWQ-32B in the R1. The R1-based SkyWork OR1 model suite delivers scalable RL training through effective data mixes and algorithm innovation.
The Minimax-M1 has led hybrid attention to scale RL efficiently, but models such as the balanced accuracy and efficiency of the Llama-Nemotron-Ultra and Magistral 24B. Improved inference has expanded use cases for coding and agent scenarios using the Claude series and Claude-4. Achieve cutting-edge results with the 1-OPUS SWE Bench Benchmark. Models like the Kimi K2 and GLM4. 5 and deepseek-v3.
1. Specifically optimized for agent tasks, demonstrating the synthesis of large-scale agent training data and general RL procedures. Multimodality is a critical component, with most frontier models including the GPT-5, O3, Claude and Gemini families, natively supporting text, images, video and audio. Open source efforts like Kimi1.5 and QVQ are excellent in visual inference, but Skywork R1v2 balances inference and general capabilities through hybrid RL. internvl3 and internvl3.
5. It has adopted a unified native multimodal training and a two-stage cascade RL framework, achieving improved efficiency and versatility. Recent models such as Step 3 and GLM-4. 5V shows cutting-edge performance across visual multimodal benchmarks, indicating the continued advancement in the inference capabilities of AI systems.
Reinforcement learning scale reasoning ability
This review shows the growing importance of reinforcement learning in developing large-scale inference models, beyond its initial role in matching language models to human preferences. Recent advances exemplified by systems such as Openai O1 and DeepSeek-R1 show that training models with verifiable rewards, such as successful mathematics and code execution, effectively enhance inference abilities such as planning, reflection, and self-correction. These models increasingly allocate computational resources during use to evaluate and refine the inference process, revealing new paths to improving performance along with traditional data and parameter scaling. This study highlights that inference itself may be explicitly trained and expanded through reinforcement learning, providing a complementary approach to pre-training methods. Although the authors have made great strides, the authors acknowledge the limitations of computational resources and algorithm design as important issues for further scaling.
