White Circle just unveiled Halo, an open-source, distributed post-training framework for models that have outgrown Huggingface TRL but don't need the full Megatron stack.

On 8× B300, Halo delivers up to ~2.8× the training throughput of stock Huggingface Transformers Reinforcement Learning (2.7× at 25% less peak memory when both sides shard ZeRO-3), with larger margins over the other frameworks benchmarked.

you can run multi-turn RL directly on Hugging Face models, from one GPU to a cluster.

Rather than reimplementing model architectures, Halo wraps existing Transformers models with expert, context, tensor and expert-tensor parallelism.