April 2026 saw a broad push to expand multi-model capability, accelerate training, and strengthen stability across the NVIDIA-NeMo/Automodel stack. Key efforts focused on expanding multi-node VLM support (Gemma4, DeepSeek V4,HYV3), enabling TransformerEngine (TE) acceleration, and improving data processing and observability. The team delivered feature completions, critical bug fixes, and robust infrastructure improvements with a clear emphasis on business value such as faster training, more resilient cross-version operation, and richer developer tooling.

March 2026 (2026-03) — Focused on reliability and performance improvements in volcengine/verl. Delivered three critical bug fixes to stabilize multimodal SFT training and training orchestration, reducing manual work and preventing training-time failures, while ensuring distributed training configurations reflect user intent. These changes improve model training reliability, reduce debugging time, and reinforce robust data/template handling across the pipeline.

January 2026 monthly summary for volcengine/verl focusing on training metrics accuracy improvements in SFTTrainer and reliable metrics reporting. The main deliverable this month was a bug fix that corrects global_tokens and total_tokens metrics so they reflect actual values during training, improving visibility into model progress and decision-making for experiments.