RLinf/RLinf

Features

• Distributed RL Scaling - Scales reinforcement learning workloads across GPU clusters by managing worker placement and asynchronous data exchange.
• Distributed Training Orchestration - Orchestrates the placement and synchronization of actors, rollout workers, and environments across GPU clusters.
• VLA - Provides a toolkit for adapting vision-language-action models to specific robotic hardware or tasks using RL and diffusion.
• Vision-Language-Action Training - Trains modular vision-language-action models to map visual and textual inputs to continuous robotic control commands.
• Distributed Worker Orchestration - Orchestrates the placement of actor and rollout processes across specific GPU nodes using configurable strategies.
• Distributed Training Coordination - Manages the flow of computation across clusters and worker groups to execute large-scale reinforcement learning.
• Agent Performance Metrics - Measures agent success using mean episode returns and trajectory length combined with visual rollout rendering.
• Reinforcement Learning Workflows - Implements reinforcement learning for training single and multi-agent systems specialized in complex reasoning and problem-solving.
• Hybrid-Partitioned Rollouts - Maximizes simulator throughput by overlapping model forward passes with environment stepping across vectorized slices.
• Dedicated Worker Group Scaling - Deploys dedicated worker groups for actors, rollouts, and environment components to scale agent training.
• Distributed Training Sharding - Shards model weights, gradients, and optimizer states across multiple nodes to overcome GPU memory limits.
• Embodied Agent Training - Executes reinforcement learning and supervised fine-tuning for models across simulated and real-world robotic environments.
• VLA Video-Model Fine-Tuning - Creates vision-language-action policies by performing supervised fine-tuning on video-generation models.
• Multi-Node Training Scaling - Distributes large-scale agent generation and training across multi-node GPU clusters to increase processing throughput.
• Reinforcement Learning Sampling - Implements sampling techniques from replay buffers to improve stability and diversity in reinforcement learning training loops.
• Hybrid Robotics Data Collection - Gathers and stores interaction data from both real-world robots and simulators into a standardized replay buffer.
• Robot Embodiment Registrations - Provides a system for registering and managing diverse robot models with standardized controllers and data layouts.
• Advantage Estimation - Optimizes robotic policies by computing normalized returns through a value model to label sample quality.
• Asynchronous Training - Decouples rollout generation from gradient updates to increase throughput in high-volume data training scenarios.
• RL Workload Orchestration - Coordinates reinforcement learning specific workloads, including rollouts and updates, across a distributed GPU cluster.
• Vision-Language Fine-Tunings - Fine-tunes vision-language models using supervised and reinforcement learning to improve reasoning and control.
• Reasoning Optimization - Applies reinforcement learning algorithms to optimize problem-solving accuracy in complex decision-making scenarios.
• Offline Reinforcement Learning - Creates reinforcement learning policies using pre-collected datasets without interacting with the live environment.
• Reinforcement Learning Training Pipelines - Manages the iterative RL lifecycle including weight syncing, rollout generation, advantage computation, and policy updates.
• Vision Model RL Workflows - Optimizes pretrained VLA models using RL objectives while keeping perception components frozen.
• Robot Policy Trainers - Refines robot control policies in simulated or real environments using PPO and offline reinforcement learning.
• VLA Policy Optimization - Optimizes robot policies using reinforcement learning for language-conditioned tasks in simulation.
• Scalable Robot Policy Trainings - Trains robot policies using reinforcement learning across diverse embodiments in GPU-accelerated simulation environments.
• Robotic Arm Training - Trains robotic arm policies to perform physical manipulation tasks using visual inputs and continuous control.
• Policy Deployments - Handoffs trained checkpoints to real-world robotic hardware or simulators for evaluation and execution.
• Training Throughput Optimization - Increases data processing speed through hybrid fine-grained pipelining and automatic online scaling.
• Reasoning Recipe Implementations - Implements reinforcement learning recipes to enhance agentic behavior and complex reasoning in large language models.
• Foundation Model Adaptation - Adapts large pre-trained foundation models for robotic tasks using supervised fine-tuning and RL alignment.
• Off-Policy Optimization - Improves agent policies using existing datasets and a value model through off-policy optimization.
• Offline Reinforcement Learning - Implements offline reinforcement learning to optimize robotic policies using historical datasets without active environment interaction.
• Robotics and Embodied AI - Provides a framework for developing agents that interact with the physical world via SFT and RL.
• Teleoperated Demonstration Collection - Gathers real-world manipulation data using teleoperation hardware to create high-quality datasets for training AI agents.
• GPU Allocations - Allocates specific GPUs to environment, rollout, and actor components to optimize resource sharing and isolation.
• Demonstration Capture Pipelines - Provides a specialized pipeline for gathering teleoperated demonstrations and sensor data into standardized replay buffers.
• Hardware-Constraint Worker Placement - Assigns worker processes to specific nodes or accelerators using a configuration file to optimize hardware utilization.
• Distributed Computing Frameworks - Coordinates the deployment of management and worker actors across a cluster to distribute computational workloads.
• Multi-Accelerator Worker Deployment - Launches computational units across multiple nodes and GPUs, automatically configuring necessary environment variables.
• Simulation Throughput Optimizers - Provides system-level optimizations and hybrid execution modes to maximize simulation throughput and reduce rollout latency.
• Accelerator Placement Mapping - Maps compute workers to specific nodes and GPUs using various execution modes to optimize resource utilization.
• Demonstration Collection Systems - Gathers real-world manipulation data via teleoperation hardware to create training datasets.
• Hardware Interfaces - Provides a driver layer for coordinating robotic arm movements and sensor data via CAN and serial communication.
• Vision-Language-Action Controllers - Maps visual observations and language prompts to continuous robotic control commands using VLM backbones.
• Soft Limit Enforcers - Prevents hardware damage by enforcing absolute joint limits, safety boxes, and clamping joint deltas.
• Memory Offloading Frameworks - Manages the movement of weights, gradients, and optimizers between memory tiers to prevent out-of-memory errors.
• Execution Pipelining - Coordinates concurrent worker tasks and data queues to maximize GPU utilization during training loops.
• In-Memory Communication Channels - Uses non-blocking producer-consumer queues to move tensors and objects between distributed workers.
• Trajectory Experience Storage - Records and indexes experience trajectories with unique identifiers and persists them for training.
• Task Success Validators - Verifies if robotic goals were achieved by monitoring task success rates through logging and video tools.
• Multi-Agent Coordination Systems - Coordinates specialized agents to collaborate on complex tasks through sub-process delegation and shared state.
• Agent Training Environment Platforms - Executes RL workflows within simulated environments to teach agents complex tasks via natural-language prompts.
• Agent Tool Execution - Provides mechanisms for training and evaluating agents by connecting them to offline and online tool backends.
• Continuous Action Generation - Produces high-frequency action sequences using noise-adding and denoising flow-matching mechanisms.
• Cross-Embodiment Transfer - Adapts the same model architecture to different robot hardware using configuration tags and unified dataset interfaces.
• Visual Reward Models - Implements a reward model that leverages wrist camera input to provide training signals based on visual task success.
• Robotic Trajectory Standardizers - Processes training data into standard layouts and supports downloading datasets for various robot embodiments.
• Implicit Q-Learning - Applies expectile regression for value functions and actor updates to implement implicit Q-learning logic.
• HuggingFace Checkpoint Loading - Loads model weights directly from HuggingFace-format files into high-performance training backends.
• Feature Scale Normalization - Calculates normalization statistics from datasets to ensure consistent feature scaling for model inputs.
• Diffusion Policy Fine-Tuning - Optimizes a frozen diffusion policy by training a lightweight agent to generate noise in the latent space.
• Tool-Use Training - Trains language models to perform multi-turn reasoning and interact with external code-execution tools.
• Human-in-the-Loop Policy Refinement - Provides a training loop that combines human-gated interventions with real-time policy updates.
• Embodiment Metadata Generation - Computes normalization statistics for robot embodiment tags to create required training metadata files.
• GPU Resource Management - Allocates workers to GPUs using collocated or hybrid strategies to optimize memory usage and throughput.
• Distributed Training - Scales the training of large model architectures across multiple compute nodes using a distributed backend.
• Classifier-Free Guidance - Steers agents toward high-advantage actions by training policy models with classifier-free guidance.
• Hardware-Aware Deployment - Launches actors on specific physical nodes and GPUs using custom environment variables and affinity scheduling.
• Model Performance Evaluators - Runs standalone evaluation scripts to quantify the accuracy and reliability of trained models using specific configurations.
• Checkpoint Format Transpilations - Saves and restores model weights in both specialized training backends and HuggingFace formats for interoperability.
• Multi-Agent Task Orchestrators - Provides coordination layers for managing multi-step information-seeking tasks using a lead agent and parallel subagents.
• Multimodal Training - Implements techniques for training models on multiple data modalities, specifically image and language observations.
• Modality-Aware Observation Pipelines - Optimizes the observation pipeline by disabling unused sensor modalities and generating data only when needed by the policy.
• Real-Time Training Interventions - Allows real-time switching between model-driven and teleoperated modes via keyboard inputs to control episode state.
• Reasoning Workflows - Executes specialized architectural patterns and recipes to enable multi-step reasoning using large language models.
• Embodiment Adaptations - Fine-tunes pre-trained VLA models on custom robot demonstrations to improve manipulation task performance.
• Dexterous Manipulation Training - Develops neural networks for robotic manipulation tasks combining arm motions with multi-finger hand control.
• Imitation Learning Trainings - Trains robot policies by learning from demonstration data to map camera images to actions.
• Supervised Fine-Tuning - Initializes student policies by training on demonstration datasets using supervised fine-tuning.
• Training Batch Generators - Ships utilities to draw rollout-aligned batches of transitions from experience windows to feed into model training.
• Automation Success Metrics - Verifies model checkpoints in simulation to measure task success signals and rewards.
• World Models & Simulation - Uses generative world models as synthetic training environments for reinforcement learning agents.
• Training Backend Switching - Toggles execution environments between lightweight prototyping and large-scale training frameworks without code changes.
• Data Checkpointing - Saves and restores the state of stored trajectories and associated metadata to disk.
• Runtime Data Exchange - Moves information between distributed workers using a producer-consumer queue with named channels.
• Experience Buffer Manipulations - Merges multiple distributed experience buffers into one or splits single buffers into smaller subsets.
• Logical-to-Resource Mappings - Provides associations between software components and specific backend cluster resources like GPUs and robots.
• Workflow Throughput Scaling - Deploys workloads across multiple GPUs and nodes using hybrid architectures to scale processing throughput.
• Remote Process Orchestration - Launches and commands groups of identical worker processes to execute functions simultaneously across a distributed system.
• Resource Placement - Assigns specific tasks to workers and defines execution modes to optimize the efficiency of processing cycles.
• Worker Groupings - Creates and manages collections of identical worker processes to execute methods across the entire group in parallel.
• Elastic Training Scaling - Automatically adjusts the scheduling and scaling of training backends to maximize resource utilization and throughput.
• Simulation Rendering Optimizations - Implements a defined horizon to alternate between high and low quality rendering for balanced simulation speed.
• Teleoperation Mappings - Translates external controller inputs into robotic joint-space coordinates for data collection and training.
• Teleoperation Systems - Implements software platforms for mapping human gestures to robotic actuators via leader-arm teleoperation for dataset creation.
• Distributed Robot Middleware - Splits computation across network nodes to decouple sensor processing from real-time robot controller execution.
• Hardware Integration - Connects training pipelines to physical robotic arms and cameras for real-world data collection and online learning.
• Joint Pose Calibrations - Determines precise joint-space coordinates for robotic grasp and lift targets using teleoperated arm data.
• Robotic Hardware Connectivity - Connects robotic arms and camera systems using software kits and serial connections for AI-driven tasks.
• Inter-Worker Tensor Transfers - Sends tensors or Python objects between workers using one-way channels that preserve operation order.
• CAN Bus Interfaces - Directs robotic arm and gripper movements using a controller area network interface and hardware-specific software kits.
• Asynchronous Flow Coordination - Tracks background data transfers using handles that support non-blocking waits and callback chaining.
• Trajectory Workload Balancing - Prevents computational bottlenecks by distributing variable-length trajectories across training groups using a round-robin strategy.
• Discounted Return Computations - Calculates cumulative discounted returns for trajectories in reverse order for RL policy optimization.
• Data Channels - Implements dedicated data channels for sending and receiving information between distributed processes.
• Distributed Cluster Coordination - Provides mechanisms for synchronizing state and retrieving node topology across a distributed session using a shared namespace.
• Distributed Barrier Synchronization - Synchronizes distributed tasks across workers using collective communication to manage job movement and checkpoints.
• Graph Execution Compilers - Accelerates training execution using graph capture and compiled code to optimize GPU processing speed.
• Hybrid Workload Placement - Provides hybrid placement strategies to distribute RL workloads across multiple GPUs and nodes for optimized throughput.
• Modular Extensibility Frameworks - Integrates custom models, environments, and algorithms into the infrastructure via a modular extension system.
• Trajectory Visualizers - Provides interactive visualizers for inspecting stored agent experience with multi-camera views and headless environment support.
• Training Metrics - Records agent performance and training progress to external backends for detailed analysis.
• Model Performance Tracking - Tracks model performance and loss during training using inference on validation datasets to monitor convergence.
• Agent Generalization Testing - Evaluates policy robustness by applying perturbations to object attributes and instructions to prevent memorization.
• Robotics Manipulation Benchmarks - Executes RL workflows across distributed workers to solve diverse robotic tasks such as stacking and cleaning.
• Distributed Worker Data Exchange - Uses asynchronous channels and collective communication to move data between distributed training workers.
• Embodied Foundation Models - Reinforcement learning infrastructure for post-training robotic policies.
• Reinforcement Learning - Library for reinforcement learning research.
• Reinforcement Learning Frameworks - Infrastructure specifically built for agentic reinforcement learning.

Star-Verlauf