# verl-project/verl

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19,272 stars · 3,263 forks · Python · apache-2.0

## Links

- GitHub: https://github.com/verl-project/verl
- Homepage: https://verl.readthedocs.io/en/latest/index.html
- awesome-repositories: https://awesome-repositories.com/repository/verl-project-verl.md

## Description

This project is a distributed training infrastructure designed for aligning large language models through reinforcement learning. It functions as an end-to-end engine for complex alignment tasks, including proximal policy optimization, direct preference optimization, and iterative self-play. By providing a unified framework for multi-turn interactions and tool-use scenarios, it enables the development of models capable of reasoning and external environment engagement.

The framework distinguishes itself through a decoupled architecture that separates model training from sample generation. This asynchronous design allows for continuous throughput by partitioning compute resources between actor, reference, and rollout models. It supports large-scale distributed execution across multi-node clusters, utilizing high-performance communication primitives to synchronize model states and aggregate losses while maintaining stability through advanced policy clipping and variance reduction techniques.

Beyond its core reinforcement learning capabilities, the system includes comprehensive infrastructure for data management, reward modeling, and performance optimization. It features modular interfaces for integrating custom tools and external reward servers, alongside built-in support for sequence parallelism, low-precision training, and hardware-specific acceleration. Observability is integrated throughout the pipeline, providing tools for profiling distributed tasks, monitoring policy divergence, and tracking GPU memory usage.

The project is implemented in Python and provides a containerized environment for deployment across diverse hardware architectures.

## Tags

### Artificial Intelligence & ML

- [Reinforcement Learning Alignment](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning-alignment.md) — Provides a distributed training infrastructure for aligning large language models using reinforcement learning techniques like PPO, GRPO, and online DPO.
- [Agentic Training Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-training-frameworks.md) — Trains language models for multi-turn reasoning and tool use by integrating interactive environments into the reinforcement learning loop.
- [Distributed Training Orchestration](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training-orchestration.md) — Manages large-scale model training across multi-node clusters with support for tensor, pipeline, and expert parallelism.
- [Reinforcement Learning Alignment](https://awesome-repositories.com/f/artificial-intelligence-ml/large-language-models/reinforcement-learning-alignment.md) — Trains large language models using reinforcement learning techniques like PPO and GRPO within distributed environments.
- [Asynchronous Rollout Decoupling](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/pipelines-and-orchestration/training-orchestration-systems/training-methodologies/reinforcement-learning-integrations/model-rollout-executions/asynchronous-rollout-decoupling.md) — Generates training samples concurrently with model updates to maintain continuous throughput and prevent hardware bottlenecks during reinforcement learning.
- [Alignment Toolkits](https://awesome-repositories.com/f/artificial-intelligence-ml/model-training-toolkits/alignment-toolkits.md) — Optimizes language model behavior through reward modeling, multi-teacher distillation, and iterative self-play fine-tuning.
- [Distributed Training](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training-frameworks/distributed-training.md) — Hosts scalable inference services that provide log-probabilities to training workers through a proxy-based request-response architecture to support large-scale model learning. ([source](https://verl.readthedocs.io/en/latest/advance/async-on-policy-distill.html))
- [Preference-Based Model Alignments](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/fine-tuning-and-alignment/preference-based-model-alignments.md) — Refines language model behavior through reward modeling, preference-based optimization, and iterative self-play.
- [Asynchronous Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/distributed-and-scaling-strategies/asynchronous-training-utilities/asynchronous-training.md) — Generates rollout samples for the next batch while simultaneously training the current batch to maximize hardware utilization and reduce idle time. ([source](https://verl.readthedocs.io/en/latest/advance/one_step_off.html))
- [Model Training Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/training-frameworks/model-training-pipelines.md) — Orchestrates complex, multi-stage training pipelines across clusters to manage model parallelism and worker synchronization.
- [On-Policy](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning/on-policy.md) — Uses a three-policy structure to decouple the proximal policy from the behavior policy, ensuring training stability. ([source](https://verl.readthedocs.io/en/latest/algo/rollout_corr_math.html))
- [Online](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning/online.md) — Executes iterative training loops by generating responses and updating policies using preference-based optimization. ([source](https://verl.readthedocs.io/en/latest/advance/dpo_extension.html))
- [Data-Parallel Training](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training-frameworks/data-parallel-training.md) — Dispatches data chunks to distributed workers and aggregates results automatically using declarative syntax to simplify parallel computation. ([source](https://verl.readthedocs.io/en/latest/hybrid_flow.html))
- [Inference Acceleration](https://awesome-repositories.com/f/artificial-intelligence-ml/inference-acceleration.md) — Provides techniques and runtimes for reducing latency and increasing throughput during model execution. ([source](https://verl.readthedocs.io/en/latest/README_vllm0.8.html))
- [Mixed Precision Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/distributed-and-accelerated-compute/training-acceleration-tools/mixed-precision-training.md) — Employs lower-bit precision formats to accelerate training speeds and reduce memory consumption. ([source](https://verl.readthedocs.io/en/latest/low_precision/fp8.html))
- [High-Performance Inference Modes](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-inference-serving/inference-optimization/high-performance-inference-modes.md) — Accelerates the rollout phase of reinforcement learning using optimized inference engines for efficient sample generation.
- [Model Training Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/model-training-frameworks.md) — Supports complex reinforcement learning workflows by maintaining distinct actor, reference, and rollout models within a unified distributed architecture.
- [Preference Optimization](https://awesome-repositories.com/f/artificial-intelligence-ml/preference-optimization.md) — Aligns language models by generating preference pairs dynamically during training to optimize responses. ([source](https://verl.readthedocs.io/en/latest/algo/spin.html))
- [Reinforcement Learning Optimizers](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning-optimizers.md) — Implements group relative policy optimization and asymmetric clipping thresholds for stable reasoning model training. ([source](https://verl.readthedocs.io/en/latest/algo/dapo.html))
- [Reinforcement Learning Value Estimators](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning-value-estimators.md) — Computes advantage estimates using generalized advantage estimation to improve the stability of policy gradient calculations. ([source](https://verl.readthedocs.io/en/latest/algo/ppo.html))
- [Custom Tool Definitions](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/integration-deployment/agent-frameworks/tool-definitions-and-registration/custom-tool-definitions.md) — Enables the implementation of stateful environment interaction tools by subclassing a base class to manage lifecycle hooks and complex multimodal input processing. ([source](https://verl.readthedocs.io/en/latest/sglang_multiturn/multiturn.html))
- [Reward Modeling](https://awesome-repositories.com/f/artificial-intelligence-ml/custom-model-training/reward-modeling.md) — Enables external sequence classification models to score responses with custom tokenization and parallel verification. ([source](https://verl.readthedocs.io/en/latest/examples/config.html))
- [Distributed Model Orchestration](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-model-orchestration.md) — Transfers model weights between actor and rollout modules using high-performance communication primitives. ([source](https://verl.readthedocs.io/en/latest/advance/fully_async.html))
- [Training Performance Profiling](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/training-monitoring-and-profiling/training-performance-profiling.md) — Captures system-level performance data including kernels and synchronization to identify training bottlenecks. ([source](https://verl.readthedocs.io/en/latest/perf/nsight_profiling.html))
- [Model Checkpoint Managers](https://awesome-repositories.com/f/artificial-intelligence-ml/model-checkpoint-managers.md) — Saves and loads sharded states for models, optimizers, and schedulers, while providing utilities to merge distributed checkpoints back into standard formats. ([source](https://verl.readthedocs.io/en/latest/workers/model_engine.html))
- [Teacher-Student Distillation](https://awesome-repositories.com/f/artificial-intelligence-ml/model-distillation-methods/teacher-student-distillation.md) — Consolidates specialized knowledge from multiple domain-specific teacher models into a single student model. ([source](https://verl.readthedocs.io/en/latest/algo/opd.html))
- [Prefix Caching](https://awesome-repositories.com/f/artificial-intelligence-ml/prompt-caching/prefix-caching.md) — Stores and shares common prompt prefixes across multiple requests to avoid redundant computation. ([source](https://verl.readthedocs.io/en/latest/perf/rollout_kv_offload.html))
- [Reinforcement Learning Data Filters](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning-data-filters.md) — Filters model outputs based on performance metrics to ensure the model learns from diverse and informative traces. ([source](https://verl.readthedocs.io/en/latest/algo/dapo.html))
- [Policy Clipping](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning-optimizers/policy-clipping.md) — Constrains probability ratio updates to prevent large policy shifts and ensure stable convergence. ([source](https://verl.readthedocs.io/en/latest/perf/best_practices.html))
- [Sequence Parallelism Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/sequence-parallelism-frameworks.md) — Distributes long-sequence data across multiple compute devices during model training to facilitate large-context processing. ([source](https://verl.readthedocs.io/en/latest/perf/perf_tuning.html))
- [AMD Hardware Acceleration](https://awesome-repositories.com/f/artificial-intelligence-ml/amd-hardware-acceleration.md) — Builds and executes containerized environments with specialized support to enable reinforcement learning workflows on specific GPU architectures. ([source](https://verl.readthedocs.io/en/latest/start/install.html))
- [Attention Kernel Configurations](https://awesome-repositories.com/f/artificial-intelligence-ml/attention-mechanisms/attention-kernel-configurations.md) — Offers configuration interfaces for selecting specialized attention kernels and backends to optimize training performance. ([source](https://verl.readthedocs.io/en/latest/advance/attention_implementation.html))
- [Reward Request Routers](https://awesome-repositories.com/f/artificial-intelligence-ml/custom-model-training/reward-modeling/reward-request-routers.md) — Coordinates requests across multiple reward model servers using a load-balancing router for unified reward computation. ([source](https://verl.readthedocs.io/en/latest/advance/reward_loop.html))
- [Distributed Training Configurations](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training-frameworks/distributed-training-configurations.md) — Configures actor, rollout, and reference models with support for gradient checkpointing and distributed training strategies. ([source](https://verl.readthedocs.io/en/latest/examples/config.html))
- [Inference Backends](https://awesome-repositories.com/f/artificial-intelligence-ml/inference-backends.md) — Provides a modular interface to integrate high-performance generation engines for flexible and scalable model rollout across diverse hardware.
- [Kernel Optimization Libraries](https://awesome-repositories.com/f/artificial-intelligence-ml/kernel-optimization-libraries.md) — Integrates high-performance computational kernels for accelerating neural network operations. ([source](https://verl.readthedocs.io/en/latest/perf/perf_tuning.html))
- [Language Model Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/language-model-fine-tuning.md) — Trains models through iterative self-play by generating synthetic data and refining policies against previous versions. ([source](https://verl.readthedocs.io/en/latest/algo/spin.html))
- [Reward Functions](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/objectives-and-optimization/mathematical-training-objectives/reward-functions.md) — Evaluates model responses using rule-based functions or external reward models to guide reinforcement learning. ([source](https://verl.readthedocs.io/en/latest/examples/ppo_code_architecture.html))
- [Policy Distillation](https://awesome-repositories.com/f/artificial-intelligence-ml/model-distillation-methods/teacher-student-distillation/policy-distillation.md) — Trains student models to match teacher output distributions on trajectories sampled during reinforcement learning. ([source](https://verl.readthedocs.io/en/latest/algo/opd.html))
- [Entropy Regulation](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning-optimizers/entropy-regulation.md) — Maintains higher entropy levels during training to encourage exploration and prevent suboptimal policy convergence. ([source](https://verl.readthedocs.io/en/latest/algo/entropy.html))
- [Reinforcement Learning Reward Systems](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning-reward-systems.md) — Calculates numerical feedback for model responses by comparing generated text against ground truth data. ([source](https://verl.readthedocs.io/en/latest/preparation/reward_function.html))
- [Delta Tokenizers](https://awesome-repositories.com/f/artificial-intelligence-ml/text-tokenization-utilities/token-optimizers/delta-tokenizers.md) — Isolates assistant-generated tokens in multi-turn conversations to ensure accurate loss masking. ([source](https://verl.readthedocs.io/en/latest/sglang_multiturn/multiturn.html))
- [AI Observability Tracing](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-observability-tracing.md) — Connects execution logs to third-party observability platforms to analyze model training performance. ([source](https://verl.readthedocs.io/en/latest/advance/rollout_trace.html))
- [Dataset Integration](https://awesome-repositories.com/f/artificial-intelligence-ml/dataset-integration.md) — Loads user-defined data structures by specifying a file path and class name to integrate custom training data into the reinforcement learning pipeline. ([source](https://verl.readthedocs.io/en/latest/examples/config.html))
- [Model Quantization Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/model-optimization/quantization/model-quantization-frameworks.md) — Reduces model size and computational requirements by converting high-precision weights into lower-precision formats. ([source](https://verl.readthedocs.io/en/latest/low_precision/nvfp4_qat.html))
- [Multimodal Input Processors](https://awesome-repositories.com/f/artificial-intelligence-ml/multimodal-input-processors.md) — Provides capabilities for ingesting and processing diverse data types including text, vision, and audio for AI agent consumption. ([source](https://verl.readthedocs.io/en/latest/workers/model_engine.html))
- [Trajectory Filtering](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning-data-filters/trajectory-filtering.md) — Excludes extreme tokens or sequences from training using rejection sampling thresholds to improve model stability. ([source](https://verl.readthedocs.io/en/latest/algo/rollout_corr_math.html))
- [Distillation Integration](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning-reward-systems/distillation-integration.md) — Integrates distillation losses with task rewards to balance policy alignment with objective performance goals. ([source](https://verl.readthedocs.io/en/latest/algo/opd.html))

### Game Development

- [Rollout Engines](https://awesome-repositories.com/f/game-development/rollout-engines.md) — Configures the rollout engine to support interactive, multi-turn conversations where the model generates multiple responses and interacts with tools. ([source](https://verl.readthedocs.io/en/latest/sglang_multiturn/multiturn.html))

### Data & Databases

- [Distributed State Synchronizers](https://awesome-repositories.com/f/data-databases/distributed-state-synchronizers.md) — Manages the transfer and consistency of model parameters between training workers and inference servers using high-performance communication primitives.
- [Data Checkpointing](https://awesome-repositories.com/f/data-databases/data-checkpointing.md) — Saves and loads model states using distributed tensor formats to ensure compatibility with large-scale parallel training and model export workflows. ([source](https://verl.readthedocs.io/en/latest/api/utils.html))
- [Training Sequence Packers](https://awesome-repositories.com/f/data-databases/typed-data-collections/sequence-management/training-sequence-packers.md) — Concatenates multiple input sequences into single batches to maximize token density and training efficiency. ([source](https://verl.readthedocs.io/en/latest/perf/perf_tuning.html))
- [Key-Value Pair Managers](https://awesome-repositories.com/f/data-databases/key-value-pair-managers.md) — Provides a high-level interface for inserting, retrieving, and managing training samples by key with support for column-level field selection. ([source](https://verl.readthedocs.io/en/latest/data/transfer_queue.html))

### DevOps & Infrastructure

- [Generation-Training Partitioning](https://awesome-repositories.com/f/devops-infrastructure/resource-allocation/generation-training-partitioning.md) — Partitions compute resources between generation and training tasks to optimize hardware usage and prevent bottlenecks. ([source](https://verl.readthedocs.io/en/latest/advance/one_step_off.html))
- [Inference Load Balancers](https://awesome-repositories.com/f/devops-infrastructure/traffic-load-balancers/inference-load-balancers.md) — Distributes rollout requests across multiple GPUs using stream-based scheduling to mitigate performance bottlenecks. ([source](https://verl.readthedocs.io/en/latest/start/agentic_rl.html))
- [Resource Allocation](https://awesome-repositories.com/f/devops-infrastructure/resource-allocation.md) — Allocates and reconfigures compute resources between generation and training tasks to optimize performance based on specific workload requirements.

### Networking & Communication

- [Distributed Training Metric Aggregators](https://awesome-repositories.com/f/networking-communication/distributed-systems-p2p/distributed-computing/hierarchical-metric-aggregation/distributed-training-metric-aggregators.md) — Normalizes and aggregates loss values across distributed compute nodes to ensure training consistency. ([source](https://verl.readthedocs.io/en/latest/api/trainer.html))
- [Remote Procedure Call Frameworks](https://awesome-repositories.com/f/networking-communication/distributed-systems-p2p/distributed-computing/remote-procedure-call-frameworks.md) — Dispatches function calls and collects results across distributed workers for seamless remote execution. ([source](https://verl.readthedocs.io/en/latest/single_controller.html))
- [Remote Procedure Calls](https://awesome-repositories.com/f/networking-communication/remote-procedure-calls.md) — Dispatches tasks and model inference requests across a distributed cluster using a unified interface for seamless cross-node communication.

### Software Engineering & Architecture

- [Streaming Data Loaders](https://awesome-repositories.com/f/software-engineering-architecture/performance-reliability/performance-optimization/data-handling-throughput/large-dataset-optimizations/streaming-data-loaders.md) — Replaces standard data loaders with a controller-managed pipeline that automatically dispatches data to training ranks without manual intervention. ([source](https://verl.readthedocs.io/en/latest/data/transfer_queue.html))

### Scientific & Mathematical Computing

- [Divergence Measures](https://awesome-repositories.com/f/scientific-mathematical-computing/numerical-mathematical-foundations/statistics-probability/probability-distributions/divergence-measures.md) — Quantifies the difference between current and reference model policies to constrain updates and ensure training stability. ([source](https://verl.readthedocs.io/en/latest/api/trainer.html))
- [Policy Divergence Monitors](https://awesome-repositories.com/f/scientific-mathematical-computing/numerical-mathematical-foundations/statistics-probability/probability-distributions/divergence-measures/policy-divergence-monitors.md) — Calculates metrics like KL divergence and perplexity ratios to quantify distribution shifts between policies. ([source](https://verl.readthedocs.io/en/latest/algo/rollout_corr_math.html))

### Security & Cryptography

- [Code Verification Sandboxes](https://awesome-repositories.com/f/security-cryptography/application-and-system-security/sandbox-and-isolation/code-sandboxing-environments/code-verification-sandboxes.md) — Executes model-generated code in secure, isolated environments to validate outputs during the reward stage. ([source](https://verl.readthedocs.io/en/latest/examples/sandbox_fusion_example.html))
- [Tokenization Validators](https://awesome-repositories.com/f/security-cryptography/security/policies/token-validation/tokenization-validators.md) — Detects and reports discrepancies in chat template processing by comparing tokenization methods. ([source](https://verl.readthedocs.io/en/latest/sglang_multiturn/multiturn.html))

### Development Tools & Productivity

- [Function Schema Generators](https://awesome-repositories.com/f/development-tools-productivity/custom-task-functions/function-schema-generators.md) — Exposes simple Python functions as model-accessible tools by automatically inferring JSON schemas from function signatures and docstrings. ([source](https://verl.readthedocs.io/en/latest/sglang_multiturn/multiturn.html))
- [Distributed Debugging](https://awesome-repositories.com/f/development-tools-productivity/distributed-debugging.md) — Inspects code execution within remote tasks by attaching debuggers to breakpoints in distributed functions. ([source](https://verl.readthedocs.io/en/latest/start/ray_debug_tutorial.html))

### System Administration & Monitoring

- [GPU Performance Monitoring](https://awesome-repositories.com/f/system-administration-monitoring/performance-monitoring-tools/gpu-performance-monitoring.md) — Logs real-time GPU memory consumption during training tasks to help identify and debug memory bottlenecks. ([source](https://verl.readthedocs.io/en/latest/api/utils.html))