How Reinforcement Learning Is Transforming the Full Lifecycle of Large Language Models

Background

In recent years, reinforcement learning (RL) has become a core training technique that markedly improves the reasoning ability, alignment performance, and instruction following of large language models (LLMs). While existing overviews touch on RL‑enhanced LLMs, they often omit a comprehensive view of how RL interacts with every stage of an LLM’s lifecycle.

Scope of the Survey

Researchers from Fudan University, Tongji University, Lancaster University, and the Chinese University of Hong Kong’s MM Lab collaborated to produce the paper “Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle.” The article offers a systematic, ~2000‑word review of the latest progress, research challenges, and future directions in this interdisciplinary field.

Lifecycle Coverage

The survey maps RL techniques onto three major phases of LLM development:

Pre‑training : RL‑based objectives that shape the initial language model representations.

Alignment fine‑tuning : RL from human feedback (RLHF) and related methods that align model outputs with user intent.

Reinforcement inference : RL‑driven decoding strategies that improve downstream reasoning and tool use.

Figure 1 (below) illustrates the core components and their interactions throughout the lifecycle.

Key Contributions

Full‑lifecycle taxonomy : A detailed classification that covers RL applications from pre‑training through alignment to inference, clarifying goals, methods, and challenges for each stage.

Focus on RL with Verifiable Rewards (RLVR) : An in‑depth analysis of the emerging RLVR paradigm, which introduces automatically verifiable reward signals to improve stability and accuracy in tasks such as mathematical reasoning and code generation.

Resource compilation : A curated list of datasets, benchmark suites, and open‑source frameworks (e.g., OpenAI‑o1, DeepSeek‑R1, RLHF toolkits) that support RL‑enhanced LLM research.

RLVR Technical Overview

RLVR augments the traditional RL loop with a reward model that can be evaluated offline, a reward‑filtering stage, and a hierarchical update mechanism. This architecture aims to provide objective, verifiable feedback that mitigates reward hacking and improves generalization.

Challenges and Future Directions

Despite impressive gains, several obstacles remain:

Scalability and training stability: Large‑scale RL on LLMs is computationally intensive and often unstable.

Reward design and credit assignment: Delayed rewards in long‑horizon reasoning create learning difficulties.

Lack of unified theoretical frameworks: Current analyses do not fully explain RL’s generalization or safety properties in LLM training.

Benchmark fragmentation: Most studies rely on task‑specific datasets, hindering fair comparison across methods.

The authors advocate for standardized benchmarks, more robust reward‑design methodologies, and deeper theoretical work to advance the field.

Reference

@misc{liu2025reinforcementlearningmeetslarge,
  title={Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle},
  author={Keliang Liu and Dingkang Yang and Ziyun Qian and Weijie Yin and Yuchi Wang and Hongsheng Li and Jun Liu and Peng Zhai and Yang Liu and Lihua Zhang},
  year={2025},
  eprint={2509.16679},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2509.16679}
}