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Revolutionizing Reinforcement Learning Framework for Diffusion Large Language Models Most comprehensive framework for dLLM's and multimodal dLLM's post-training
The main features of gen-verse/dllm-rl are: Reinforcement Learning Frameworks, Training and Alignment.
Projects with overlapping indexed features include: google/dopamine — Dopamine is a reinforcement learning research framework designed for prototyping and testing algorithms across diverse… facebookresearch/habitat-lab — Habitat-Lab is an open-source platform for training and evaluating embodied AI agents in photorealistic 3D indoor… alibaba/roll — ROLL is a distributed reinforcement learning framework and model alignment toolkit designed for large language models.… apple/ml-diffucoder — This software project accompanies the research paper, DiffuCoder: Understanding and Improving Masked Diffusion Models… aunum/gold — Reinforcement Learning in Go. amap-ml/ar-map — Are Autoregressive Large Language Models Implicit Teachers for Diffusion Large Language Models? A comprehensive…
Dopamine is a reinforcement learning research framework designed for prototyping and testing algorithms across diverse simulated environments. It provides an agent development toolkit that utilizes a flat class hierarchy to facilitate the creation and extension of learning agents. The framework includes a standardization layer via environment wrappers that connect agents to various physics simulations and gaming environments. It also features a high-performance experience replay buffer for storing and sampling transition data to improve training stability, alongside a dedicated hyperparameter
Habitat-Lab is an open-source platform for training and evaluating embodied AI agents in photorealistic 3D indoor environments. It functions as a high-performance 3D indoor environment simulator that supports physics-based interaction, enabling research into navigation and manipulation tasks. The platform provides a modular task-environment abstraction that separates task logic from environment simulation, using configuration-driven pipeline assembly to compose simulation and training pipelines. It includes a hierarchical sensor-actuator architecture for mixing and matching perception and act
ROLL is a distributed reinforcement learning framework and model alignment toolkit designed for large language models. It serves as a scalable training pipeline and GPU cluster manager, providing the infrastructure to align model behavior using reinforcement learning algorithms and preference optimization techniques. The project distinguishes itself through an agentic rollout orchestrator that generates and collects multi-turn interaction trajectories between AI agents and simulated environments. It supports specialized alignment methods including Direct Preference Optimization, reinforcement
Are Autoregressive Large Language Models Implicit Teachers for Diffusion Large Language Models? A comprehensive framework for transferring alignment knowledge from AR-LLMs to Diffusion Models