repoGitHubTrust 82 · PrimaryPublished 12d agoLive · 15h ago
pytorch/rl
A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 52%RL without TD learning →
- PossiblePossibly related (embedding) · 51%Z-1: Efficient Reinforcement Learning for Vision-Language-Action Models →
- PossiblePossibly related (embedding) · 51%Regularized Reward-Punishment Reinforcement Learning →
- PossiblePossibly related (embedding) · 49%Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training →
- PossiblePossibly related (embedding) · 49%Automating Potential-based Reward Shaping with Vision Language Model Guidance →
- PossiblePossibly related (embedding) · 45%Guiding generative models to uncover diverse and novel crystals via reinforcement learning →
- PossiblePossibly related (embedding) · 47%A Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation →
- PossiblePossibly related (embedding) · 50%[2607.07508] Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning →
Covers
Implements
paperZ-1: Efficient Reinforcement Learning for Vision-Language-Action ModelspaperRegularized Reward-Punishment Reinforcement LearningpaperIs One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL TrainingpaperAutomating Potential-based Reward Shaping with Vision Language Model Guidance
Covers (incoming)
Implements (incoming)
Related across the graph
news[2607.07508] Single-Rollout Asynchronous Optimization for Agentic Reinforcement LearningnewsRL without TD learningpaperRegularized Reward-Punishment Reinforcement LearningnewsGuiding generative models to uncover diverse and novel crystals via reinforcement learningpaperAutomating Potential-based Reward Shaping with Vision Language Model GuidancenewsThe Little Book of Reinforcement LearningpaperRing-Zero: Scaling Zero RL to a Trillion Parameters for Emergent ReasoningpaperIs One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL TrainingpaperA Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous ManipulationpaperZ-1: Efficient Reinforcement Learning for Vision-Language-Action Models