Jet-RL: Enabling On-Policy FP8 Reinforcement Learning with Unified Training and Rollout Precision Flow
Jet-RL framework improves RL training efficiency with unified FP8 precision flow, achieving 33% rollout speedup.
Key Findings
Methodology
Jet-RL addresses numerical mismatch between training and inference by adopting a unified FP8 precision flow. This method eliminates unnecessary calibration steps, ensuring training stability and efficiency. Its core lies in unifying the precision for both training and rollout to FP8, reducing numerical errors.
Key Results
- Achieved up to 33% speedup in the rollout phase and 41% in the training phase. Compared to BF16 training, Jet-RL achieved a 16% end-to-end speedup while maintaining stable convergence and negligible accuracy degradation.
- Jet-RL demonstrated stable performance across various tasks and model settings, especially in long-sequence generation tasks, outperforming the traditional BF16-train-FP8-rollout strategy.
- By eliminating numerical mismatch between training and inference, Jet-RL significantly reduces the risk of training instability and accuracy collapse.
Significance
Jet-RL holds significant importance in academia and industry as it addresses the computational bottleneck in RL training, especially in complex reasoning tasks of large language models. By enhancing training efficiency and stability, Jet-RL enables rapid iteration and deployment of large-scale models.
Technical Contribution
Jet-RL's technical contribution lies in the first implementation of a unified FP8 precision flow for both training and rollout. This approach not only improves computational efficiency but also ensures training stability. Compared to existing BF16-train-FP8-rollout methods, Jet-RL offers higher precision and faster convergence.
Novelty
Jet-RL is the first method to implement a unified FP8 precision flow in RL training. Compared to the traditional BF16-train-FP8-rollout strategy, Jet-RL significantly enhances training stability and efficiency by eliminating numerical mismatch.
Limitations
- In extreme long-sequence generation tasks, although Jet-RL performs well, it may still face the limits of numerical precision.
- In certain complex tasks, FP8 precision may not capture subtle numerical changes.
Future Work
Future research could explore the applicability of Jet-RL to more tasks and models, especially larger-scale models. Additionally, further optimization of FP8 precision to support more complex tasks could be studied.
AI Executive Summary
The Jet-RL framework addresses the computational bottleneck in reinforcement learning training through a unified FP8 precision flow. Existing BF16-train-FP8-rollout strategies perform poorly in long-sequence generation tasks. Jet-RL significantly enhances training stability and efficiency by eliminating numerical mismatch between training and inference.
The core innovation of Jet-RL lies in adopting a unified FP8 precision flow, which not only accelerates the rollout phase but also achieves significant speedup in the training phase. Experimental results show that Jet-RL maintains stable convergence and accuracy across various tasks and model settings.
This method holds significant importance in academia and industry as it enables rapid iteration and deployment of large-scale models. However, Jet-RL may still face the limits of numerical precision in extreme long-sequence generation tasks. Future research could explore its applicability to larger-scale models.
Deep Analysis
Background
Reinforcement learning (RL) is crucial for enhancing the complex reasoning capabilities of large language models (LLMs). However, existing RL training pipelines are computationally inefficient, with the rollout phase consuming over 70% of total training time. Quantized RL training, particularly using FP8 precision, offers a promising approach to mitigate this bottleneck.
Core Problem
The existing BF16-train-FP8-rollout strategy suffers from training instability and catastrophic accuracy collapse in long-sequence generation and challenging tasks. This is due to numerical mismatch between training and inference, leading to significant performance degradation.
Innovation
Jet-RL addresses numerical mismatch by adopting a unified FP8 precision flow for both training and rollout. Unlike traditional methods, Jet-RL eliminates unnecessary calibration steps, ensuring training stability and efficiency.
Methodology
- �� Adopt a unified FP8 precision flow to reduce numerical errors.
- �� Eliminate numerical mismatch between training and inference.
- �� Enhance computational efficiency in rollout and training phases.
Experiments
Experiments were conducted on multiple models and datasets, including Llama3.1-8B and Qwen3-8B-Base. By comparing BF16 training with Jet-RL, the speedup in rollout and training phases was validated.
Results
Jet-RL achieved a 33% speedup in the rollout phase and a 41% speedup in the training phase. Compared to BF16 training, Jet-RL achieved a 16% end-to-end speedup while maintaining stable convergence and negligible accuracy degradation.
Applications
Jet-RL can be directly applied to large-scale language model training requiring efficient inference, especially in complex tasks requiring long-sequence generation.
Limitations & Outlook
While Jet-RL performs well in most tasks, it may face the limits of numerical precision in extreme long-sequence generation tasks. Additionally, FP8 precision may not capture subtle numerical changes in certain complex tasks.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen. Traditional RL training is like following a complex recipe with many steps and tools, taking a lot of time. Jet-RL is like using a simple recipe where all steps use the same tool and method, saving time and reducing errors. By using a unified tool (FP8 precision), Jet-RL makes the whole process more efficient and stable.
ELI14 Explained like you're 14
Imagine you're playing a game with many steps to complete. Traditional methods are like using different tools for each step, while Jet-RL is like using the same tool for all steps, allowing you to finish the game faster without mistakes. Jet-RL is a smart way to make complex tasks simple and efficient!
Glossary
FP8 Precision
A low-precision floating-point representation used to accelerate computation.
Used in Jet-RL for unified training and inference precision flow.
Rollout Phase
The sequence generation process in RL training, often a computational bottleneck.
Jet-RL accelerates the rollout phase to improve training efficiency.
BF16 Precision
A floating-point precision commonly used in training for higher numerical stability.
Used in traditional RL training to maintain training stability.
Numerical Mismatch
Numerical errors caused by using different precisions in training and inference.
Jet-RL addresses this issue by unifying precision flow.
Large Language Models
Deep learning models capable of handling complex language tasks.
Jet-RL is used to enhance the training efficiency of large language models.
Open Questions Unanswered questions from this research
- 1 How can Jet-RL be applied to larger-scale models?
- 2 How does FP8 precision perform in extremely complex tasks?
Applications
Immediate Applications
Large-scale Model Training
Jet-RL can be used to accelerate the training of large-scale language models, especially in tasks requiring long-sequence generation.
Long-term Vision
General AI Systems
By improving training efficiency, Jet-RL contributes to the development of more powerful general AI systems.
Abstract
Reinforcement learning (RL) is essential for enhancing the complex reasoning capabilities of large language models (LLMs). However, existing RL training pipelines are computationally inefficient and resource-intensive, with the rollout phase accounting for over 70% of total training time. Quantized RL training, particularly using FP8 precision, offers a promising approach to mitigating this bottleneck. A commonly adopted strategy applies FP8 precision during rollout while retaining BF16 precision for training. In this work, we present the first comprehensive study of FP8 RL training and demonstrate that the widely used BF16-training + FP8-rollout strategy suffers from severe training instability and catastrophic accuracy collapse under long-horizon rollouts and challenging tasks. Our analysis shows that these failures stem from the off-policy nature of the approach, which introduces substantial numerical mismatch between training and inference. Motivated by these observations, we propose Jet-RL, an FP8 RL training framework that enables robust and stable RL optimization. The key idea is to adopt a unified FP8 precision flow for both training and rollout, thereby minimizing numerical discrepancies and eliminating the need for inefficient inter-step calibration. Extensive experiments validate the effectiveness of Jet-RL: our method achieves up to 33% speedup in the rollout phase, up to 41% speedup in the training phase, and a 16% end-to-end speedup over BF16 training, while maintaining stable convergence across all settings and incurring negligible accuracy degradation.