OpenRLHF/OpenRLHF

Features

• Distributed Training - Provides a distributed framework for training massive models using sharding and sequence parallelism across GPU clusters.
• Reinforcement Learning Alignment - A distributed framework for aligning large language models using RLHF algorithms like PPO and GRPO across GPU clusters.
• LLM Fine-Tuning Engines - Provides a specialized engine for efficient distributed fine-tuning of large language models using parameter sharding.
• Reward Modeling - Provides tools to train scalar reward models that evaluate output quality to provide feedback for reinforcement learning.
• Distributed Inference Engines - Implements a distributed inference engine that overlaps sample rollout with training to maximize GPU throughput.
• Distributed Training Sharding - Supports parameter sharding across distributed clusters to enable training of models exceeding 70 billion parameters.
• Large Language Model Fine-Tuning - Performs supervised fine-tuning and low-rank adaptation to specialize base models for specific tasks.
• Multimodal Alignment - Applies reinforcement learning to multimodal vision-language models to improve responses based on image inputs.
• Preference-Based Model Alignments - Aligns large language models with human preferences using RLHF, PPO, GRPO, and DPO algorithms.
• Reward Functions - Supports the definition of custom reward functions via Python or remote HTTP calls to guide the alignment process.
• Reinforcement Learning Integrations - Implements reinforcement learning algorithms like PPO and GRPO to refine model responses based on human preferences.
• Asynchronous Rollout Decoupling - Implements a pipeline that decouples sample generation from gradient updates to maximize GPU throughput during reinforcement learning.
• Model Training and Inference Engines - Provides a unified engine that integrates both inference serving and training loops on the same device for real-time updates.
• Large Language Model Training Frameworks - Ships a distributed framework designed specifically for training and aligning large language models across GPU clusters.
• Parameter Efficient Fine-Tuning - Provides low-rank adaptation (LoRA) to reduce memory and compute during supervised fine-tuning and reward modeling.
• Preference Optimization - Implements direct preference optimization (DPO) and similar algorithms to align models with human preferences without a separate reward model.
• RL Training Workflows - Provides standard RL training workflows for single-turn generation using reward models or custom Python functions.
• RLHF Alignment Algorithms - Implements a suite of alignment algorithms including PPO, GRPO, and RLOO to optimize model behavior via reward signals.
• Supervised Fine-Tuning - Provides supervised fine-tuning capabilities to initialize models for subsequent preference learning and alignment.
• Distributed Training Coordination - Coordinates multi-node training processes and manages resumable checkpoints for large-scale production runs.
• Multi-turn Interaction Managers - Supports both single and multi-turn interaction pipelines by separating the learning algorithm from execution mode.
• Agentic Interaction Training - Trains interactive models capable of complex reasoning through multi-step environment interactions.
• Multi-Turn Reinforcement Learning - Supports multi-turn reinforcement learning for complex reasoning tasks through multi-step environment interactions.
• Sequence Packing - Includes a data loader that packs multiple short sequences into fixed-length blocks to eliminate padding waste and increase throughput.
• Cross-Hardware Workload Distribution - Allows allocating specific hardware groups to different model roles across mixed GPU clusters.
• Generation Accelerators - Increases throughput by overlapping experience sample rollout with the training process using a distributed inference engine.
• Vision-Language Trainers - Extends RLHF capabilities to multimodal models, allowing alignment based on image inputs and visual feedback.
• Long Context Processing - Processes sequences exceeding 8K tokens using ring-attention and sequence parallelism across the compute cluster.
• Resource Colocation Strategies - Implements dynamic role-swapping to share GPU resources between different model components on the same device.
• Asynchronous Training - Prevents compute bottlenecks by overlapping data generation and model training using asynchronous queues.
• Low-Rank Adaptation - Integrates low-rank adaptation (LoRA) to reduce memory and compute requirements during the model alignment process.
• Model Role Colocation - Maximizes GPU utilization by colocating different model roles on the same device and swapping them dynamically.
• Sequence Parallelism Frameworks - Implements ring-attention sequence parallelism to distribute long-context sequences across multiple GPUs and bypass memory limits.
• Model Component Colocation - Optimizes memory on small clusters by colocating model components and sharing resources via sleep-mode.
• Optimizer State Offloading - Ships a mechanism to offload optimizer states to CPU RAM, enabling larger batch sizes on limited GPU hardware.
• Memory Offloading Frameworks - Reduces GPU memory footprint through gradient checkpointing and offloading optimizer states to secondary storage.
• Critic-Free Algorithms - Robust reinforcement learning algorithm for human feedback alignment.
• Model Training - Framework for scalable reinforcement learning from human feedback.
• Model Training Frameworks - Scalable framework for high-performance reinforcement learning from human feedback.
• Preference Alignment - Listed in the “Preference Alignment” section of the Llm Course awesome list.
• Reinforcement Learning - Framework for reinforcement learning from human feedback.
• Reinforcement Learning Frameworks - Comprehensive framework for reinforcement learning from human feedback.
• Training and Fine-Tuning - High-performance RLHF framework.

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