@SergioPaniego: continuous batching just landed in TRL for GRPO at 64 generations it runs faster and uses less VRAM than plain generate…
Summary
Continuous batching has been added to TRL for GRPO, improving speed and VRAM usage without needing vLLM. The tweet explains how it works and when to use it.
View Cached Full Text
Cached at: 06/20/26, 02:36 PM
continuous batching just landed in TRL for GRPO
at 64 generations it runs faster and uses less VRAM than plain generate, no vLLM needed
how it works and when to reach for it, below
Similar Articles
@QGallouedec: TRL v1.4 is out! two things I'm excited about: → chunked NLL loss for SFT. Way less VRAM, same loss, often faster. Qwen…
TRL v1.4 is released, featuring chunked NLL loss for SFT to reduce VRAM usage and first-class integration with OpenReward for GRPO.
LithoGRPO: Fast Inverse Lithography via GRPO Reinforced Flow Matching
LithoGRPO introduces a novel framework that combines flow matching with GRPO-based reinforcement learning for fast and high-quality inverse lithography mask optimization, achieving state-of-the-art performance while maintaining efficient generation.
Unlocking asynchronicity in continuous batching
This article explains how to implement asynchronous continuous batching for LLM inference, overlapping CPU batch preparation with GPU computation to maximize utilization and reduce idle time.
@akshay_pachaar: https://x.com/akshay_pachaar/status/2064700531600458093
This article explains how to use GRPO to fine-tune an LLM (Qwen3-8B) for reliable JSON structured output, improving schema accuracy from 62% to 82%, surpassing GPT-4.1's 58%.
@SergioPaniego: https://x.com/SergioPaniego/status/2067270222671741360
OpenReward environments now integrate directly into TRL's GRPOTrainer via a single OpenRewardSpec, allowing zero-glue-code training against a catalog of RL environments. The integration is experimental and part of a broader effort to make environment and agent RL first-class in TRL.