Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle
This paper surveys how reinforcement learning can improve large language models, which are AI systems that understand and generate text. It discusses the methods used, the challenges faced, and the resources available for researchers.
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- 1 The objective of the agent is to learn an optimal policy ๐ * that maximizes the expected long-term cumulative reward over the course of interactions.
- 2 It directly estimates the gradient of the expected return with respect to the policy parameters, where the objective is defined as ๐ฝ (๐ ) = E[๐ ], with ๐ denoting the cumulative return.
- 3 Through experiments, Cui et al. showed that although entropy values need to be controlled, it is possible to design objective functions that are superior to entropy loss.
- 4 AdaptThink showed that skipping reasoning benefits simple tasks in performance and efficiency, and by constraining the objective with importance sampling, it balances "thinking" and "non-thinking" samples during training to enable adaptive mode selection.
Introduction
Large Language Models such as ChatGPT have risen rapidly demonstrating remarkable performance across various tasks, including general dialogue , code generation , and mathematical reasoning , and have gradually become essential cornerstones for interactive artificial intelligence systems . Moreover, several recent studies have indicated that the reasoning capabilities of LLMs still exhibit substantial shortcomings.
This table compares representative models trained with RL against their baseline counterparts, showing that RL substantially enhances the performance of foundation models and underscoring the critical importance of reinforcement learning.
Since the seminal introduction of Reinforcement Learning from Human Feedback (RLHF) by Ouyang et al. ,.
Although RL has already been applied to multimodal reasoning, its reasoning process is still mainly limited to text forms .
Several methods have been proposed to address the limitation that models often struggle to effectively anchor their reasoning on visual cues.
Research Question
The objective of the agent is to learn an optimal policy ๐ * that maximizes the expected long-term cumulative reward over the course of interactions. It directly estimates the gradient of the expected return with respect to the policy parameters, where the objective is defined as ๐ฝ (๐ ) = E[๐ ], with ๐ denoting the cumulative return.
The formal objective is given by: max.
The core contribution is a clipped surrogate objective that allows multi-step gradient updates without policy collapse.
Methodology
Therefore, effectively aligning the generative capabilities of LLMs with human preferences, values, and specific task requirements, as well as enhancing their reasoning abilities for addressing complex problems, has emerged as one of the significant challenges in current LLM research. RL-based fine-tuning has become a cornerstone method for improving LLM alignment with human instructions and preferences.
Study Design
Specifically, we offer in-depth analysis and discussion along multiple dimensions: (1) the theoretical foundations of applying RL to LLMs; (2) and (4) the emerging tools and frameworks that support large-scale RL training for LLMs.
Pternea et al. discuss the synergy between RL and LLMs, but their analysis is largely limited to the perspective of bidirectional RL-LLM collaboration.
Pternea et al. discuss the synergy between RL and LLMs, but their analysis is largely limited to the perspective of bidirectional RL-LLM collaboration.
Analysis of coverage and perplexity indicates that all reasoning paths generated by RLVR exist in the sampling distribution of the base model, suggesting that its capabilities are inherently derived from and limited by the base model.
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Results & Findings
Despite their broad generalization capabilities, current LLMs still struggle with crucial shortcomings: they often fail to reliably capture nuanced human intentions and can produce misleading or unsafe outputs . In response, RL has been introduced as a powerful framework to address these challenges by directly optimizing model behavior through interactive feedback and reward signals.
- Despite their broad generalization capabilities, current LLMs still struggle with crucial shortcomings: they often fail to reliably capture nuanced human intentions and can produce misleading or.
- In response, RL has been introduced as a powerful framework to address these challenges by directly optimizing model behavior through interactive feedback and reward signals.
- Among them, Magistral Small-SC * and Magistral Small-RL # refer to Magistral Small-24B-Starting Checkpoint and the result of this model trained only through reinforcement learning, respectively.
- By leveraging human evaluative feedback or learned reward models, RLHF enables models to iteratively adjust their outputs toward more preferred and helpful responses, going beyond what.
- High-profile examples include OpenAI’s o1 system , Anthropic’s Claude 3.7/4 ,.
Rather than relying solely on outcome-based reward signals, future work is expected to incorporate process-level supervision and intermediate rewards that evaluate the quality of reasoning steps, justification logic, or adherence to constraints throughout the generation process .
By organizing the survey along these axes, we aim to provide researchers and practitioners with a clear roadmap of the field’s current state, insights into the efficacy and limitations of various RL techniques especially RLVR, and well-supported guidance for future.
Practical Applications
By adjusting the network parameters, ๐ (๐ , ๐; ๐ ) outputs value estimates for all possible actions given a state input. This two-stage process is complex and may be unstable.
Unlike constrained single-turn text generation, an autonomous LLM agent could take a sequence of harmful or undesirable actions if its reward function is misspecified .
By embedding logical or graph-structured inductive biases into the RL process, models may learn reasoning strategies that transfer more robustly to new tasks, as opposed to the relatively unstructured trial-and-error approach currently prevalent.
Contribution Summary
The survey presents three main contributions: a lifecycle organization of RL techniques for LLMs, a focus on advanced RL with verifiable rewards (RLVR), and a consolidation of resources including datasets and benchmarks for RL-based experimentation.
Preliminaries of Reinforcement Learning
This section introduces the fundamentals of reinforcement learning, including its modeling as a Markov Decision Process (MDP), the objective of maximizing cumulative rewards, and the two primary paradigms: policy-based and value-based learning.
Frequently Asked Questions
The objective of the agent is to learn an optimal policy ๐ * that maximizes the expected long-term cumulative reward over the course of interactions. It directly estimates the gradient of the expected return with respect to the policy parameters, where the.
Pternea et al. discuss the synergy between RL and LLMs, but their analysis is largely limited to the perspective of bidirectional RL-LLM collaboration. We provide an in-depth analysis of the experimental phenomena and cutting-edge applications of RLVR, exploring the methodologies used to.
Finally, the question of how to implement RL fine-tuning efficiently at scale without destabilizing the model’s performance is still not fully resolved. To achieve this objective, RL algorithms have evolved along two primary paradigms: policy-based and value-based learning.
Therefore, a crucial aspect of embodied intelligence tasks is that the model needs to possess the ability to perceive and understand spatial relationships from first-person perspective video streams. Unlike constrained single-turn text generation, an autonomous LLM agent could take a sequence of.
By organizing the survey along these axes, we aim to provide researchers and practitioners with a clear roadmap of the field’s current state, insights into the efficacy and limitations of various RL techniques especially RLVR, and well-supported guidance for future work in.
This paper surveys how reinforcement learning can improve large language models, which are AI systems that understand and generate text. It discusses the methods used, the challenges faced, and the resources available for researchers.