CRL-LLM: A Fair Comparative Study of LLMs Under a Controllable Reinforcement Learning Framework

This article introduces the CRL-LLM project released by SAMG669 on GitHub (May 26, 2026), which aims to address the problem of experimental variable interference in LLM reinforcement learning comparisons. By building a standardized PPO training environment and unifying six key dimensions including prompt datasets, reward functions, and hyperparameters, it enables performance comparison of models like Qwen and LLaMA under the same conditions, reveals inherent model differences, and provides a reliable benchmark for LLM reinforcement learning research.

Reinforcement learning fine-tuning of large language models (LLMs) is key to improving their practicality. However, current cross-model comparisons are interfered by external factors such as training environments, hyperparameters, and reward functions, making it difficult to determine the source of performance differences. This problem is particularly prominent in PPO training, where inconsistent experimental conditions among different teams lead to unreliable results. The CRL-LLM project was thus born to build a controllable reinforcement learning experimental framework.

The core of CRL-LLM is standardization, eliminating external interference through six unified dimensions:

1. Shared prompts/datasets
2. Unified reward function
3. Unified PPO hyperparameters
4. Unified training process
5. Unified evaluation method
6. Unified GPU environment This ensures that performance differences are attributed to the inherent characteristics of the models themselves.

1. Standardized PPO fine-tuning pipeline: Covers data loading, policy network initialization, and other links, supporting distributed training of large-scale models.
2. Cross-model family comparison: Verified to support Qwen (Alibaba) and LLaMA (Meta) series models.
3. Learning behavior analysis: Provides multi-dimensional analysis tools such as reward curves, policy evolution, and convergence speed.

Research Value: Provides a reproducible and comparable benchmark platform, supporting the verification of new architectures, analysis of optimization strategies, etc. Application Scenarios: Reinforcement learning basic research, model selection decisions, training process optimization, academic reproduction, industrial evaluation workflows.

CRL-LLM reveals that "controlling variables" is a core requirement for machine learning research, and a standardized experimental environment is the key to moving from "alchemy" to "science". Insight for developers: First establish a comparable evaluation system, then pursue performance optimization.

CRL-LLM solves the comparability problem in LLM reinforcement learning comparisons through a standardized PPO environment. Its "six unifications" design has academic value and industrial practical value, and will become an important infrastructure for large model research.