# TitanRL

TorchTitan's annual update adds PyTorch-native training support for open-weight models including Muse Spark Open, Qwen3.5, Kimi K3 and DeepSeek-V4, and introduces TitanRL, which keeps one model from pretraining through post-training to inside vLLM so training and inference no longer run two copies that can quietly disagree. In supported on-policy configurations the trainer and generator produce bitwise identical log probabilities, leaving exactly zero gap at every step. The update also reports DeepSeek V3 671B throughput on the MLPerf training benchmark and infrastructure work around FlexShard and torch_checkpointing.

TorchTitan's annual update adds PyTorch-native training support for open-weight models including Muse Spark Open, Qwen3.5, Kimi K3 and DeepSeek-V4, and introduces TitanRL, which keeps one model from pretraining through post-training to inside vLLM so training and inference no longer run two copies that can quietly disagree. In supported on-policy configurations the trainer and generator produce bitwise identical log probabilities, leaving exactly zero gap at every step. The update also reports DeepSeek V3 671B throughput on the MLPerf training benchmark and infrastructure work around FlexShard and torch_checkpointing.