carperai/trlx
Trlx
trlx is a reinforcement learning library and training framework designed to align large language models using human feedback. It serves as a distributed trainer and compute orchestrator for scaling high-parameter models across multiple GPUs and nodes.
The project provides tools for reinforcement learning from human feedback and model alignment. It implements reward-model-based optimization and proximal policy optimization to refine model behavior based on goal-oriented rewards or human-labeled datasets.
The framework covers distributed training strategies, including model parallelism, parameter sharding, and multi-node gradient synchronization. It also incorporates constraints like KL-divergence to manage model drift during the reinforcement learning process.
Features
- RLHF Alignment Algorithms - Implements RLHF alignment algorithms like PPO to align model outputs with human preferences.
- Reward Modeling - Provides reward modeling capabilities to evaluate and score text generation for preference alignment.
- Reinforcement Learning Fine-Tuning - Fine-tunes language models using reward signals from human feedback and RL alignment algorithms.
- Language Model Trainers - Serves as a specialized trainer for updating the parameters of large language models across distributed nodes.
- Distributed Training - Scales the training of high-parameter language models across multiple compute nodes to manage memory and time.
- Reinforcement Learning Optimizers - Optimizes language model behavior and performance using reward functions and RL algorithms.
- Transformer Reinforcement Learning Libraries - Provides a library of tools for fine-tuning and aligning transformer models using reinforcement learning.
- RLHF Training Pipelines - Offers end-to-end training pipelines that integrate reward modeling with PPO-based policy optimization.
- Proximal Policy Optimization Alignment - Implements Proximal Policy Optimization with a clipped objective function to ensure stable reinforcement learning updates.
- Distributed Model Parallelism - Employs distributed model parallelism to partition computational workloads across multiple GPUs for massive models.
- Distributed Gradient Synchronization - Coordinates gradient updates across a cluster of machines using collective communication patterns to maintain consistency.
- Data Parallelism - Implements data parallelism to split training batches across multiple GPU nodes for faster model convergence.
- Distributed Device Orchestration - Orchestrates computational loads and memory across multiple hardware devices during large-scale model refinement.
- Distributed Parameter Sharding - Partitions large model tensors across multiple compute nodes to fit high-parameter architectures in memory.
- KL-Divergence Penalties - Uses KL-divergence penalties to prevent the RL-tuned policy from drifting too far from the reference model.
- Open Source Models - Facilitates distributed training with human feedback.
- Reinforcement Learning Tools - Framework for fine-tuning models with reinforcement learning.
- RLHF Frameworks - Distributed training framework for fine-tuning models with PPO and ILQL.
Star history
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