June 2026 performance highlights focused on reliability, correctness, and scalable training for MoE and context-parallel workflows. In NVIDIA-NeMo/Automodel, fixed floating-point precision handling for custom MoE models under Fully Sharded Data Parallel (FSDP), ensuring correct mixed-precision policy application, proper dtype casts of expert weights, and stable forward passes across model configurations. This work included propagating user-defined precision settings through components to prevent crashes and maintain consistent behavior across full-EP and partial-EP setups. The changes reduced failure modes during MoE inference/training and preserved dtype semantics through initialization and checkpoint loading. In NVIDIA/NeMo-RL, added Qwen3.5 dense and MoE model support for GRPO training with context parallelism enhancements, supported by new configuration files for model training. These updates extend scalable training options and improve throughput for large models. Overall impact: increased training stability, better dtype consistency, and expanded capabilities for MoE and context-parallel training, enabling higher reliability and reproducibility in production and research workflows. Technologies/skills demonstrated: Fully Sharded Data Parallel (FSDP), mixed-precision policies, dtype threading and propagation across MoE components, checkpoint dtype preservation, and context-parallel GRPO training integration with Qwen3.5 models.

May 2026 monthly summary focusing on key accomplishments and business impact. Two core feature updates were delivered across NVIDIA/NeMo projects, delivering measurable improvements in training efficiency, compatibility, and model deployment readiness. Key features delivered: - NVIDIA/NeMo-RL: Transformer Engine upgrade to 2.14.1 and enablement of TE FusedAdam optimizer for Qwen3.5 MoE and GLM-4.7-Flash automodel recipes, boosting training efficiency and model performance. Commits: f947d78fb5b4b26106802e2d2c6e95119522eeec; 1a97e0fe9566eb6412d51811fcf042ecb6c8cabf - NVIDIA-NeMo/Automodel: Base FP8 Expert Layout Support in DeepSeek V4, enabling improved handling of quantized weights and improved deployment flexibility. Commit: ff8d75d5160e4619946041235469afbe68f79f6a Major bugs fixed: - Enable TE FusedAdam for Qwen3.5 MoE & GLM-4.7-Flash automodel recipes (commit 1a97e0fe9566eb6412d51811fcf042ecb6c8cabf) - DeepSeek-V4: Base FP8 expert layout support fix to ensure compatibility with Flash-Base workflows (commit ff8d75d5160e4619946041235469afbe68f79f6a) Overall impact and accomplishments: - Improved training throughput and efficiency across large-scale RL and automodel training pipelines through Transformer Engine upgrade and FusedAdam optimization. - Enhanced quantization handling and deployment flexibility with DeepSeek V4 FP8 expert layout support. - Maintained code quality and readability with formatting improvements, supporting long-term maintainability. Technologies/skills demonstrated: - Transformer Engine (2.14.1), FusedAdam optimizer, Qwen3.5 MoE, GLM-4.7-Flash automodels - FP8 quantization, DeepSeek V4, Ruff formatting, code quality discipline - Cross-repo collaboration and release hygiene (bumped dependencies, fixed layout support)

April 2026 performance summary for NVIDIA/NeMo projects. Focused on expanding model support, optimizing training workflows, and improving downstream accessibility through packaging changes. Delivered significant feature enhancements across NeMo-RL and Automodel, enabling broader adoption and faster iteration in production environments.

March 2026: FP8 End-to-End Training Support delivered for volcengine/verl. Implemented FP8 block quantization padding in the EngineWorker to align sequence lengths for FP8 E2E training, added new padding controls in preprocessing, and ensured FP8 configuration is read and applied in the forward step. Updated FP8 docs to cover End-to-End training configuration and reinforcement learning results. Fixed FP8 padding gaps in EngineWorker preprocess paths to mirror the legacy padding logic, addressing alignment issues that caused Float8BlockQuantizer assertions. Propagated use_fp8_padding across preprocessing and forward calls (model_forward.py, model_forward_fused.py, transformer_impl.py). Documentation improvements reorganized the FP8 guide into FP8 Rollout Only and FP8 End-to-End with E2E configuration and Qwen3-30B-A3B results. Overall impact: increased reliability and readiness for FP8 RL workloads, enabling better performance and cost efficiency in E2E FP8 training. Technologies demonstrated: FP8 quantization, EngineWorker integration, padding alignment, forward-path configuration, cross-module coordination, and documentation discipline.

February 2026 monthly summary focused on delivering measurable business value through targeted feature work and critical bug fixes across two repositories. Highlights include performance-oriented quantization optimization and correctness hardening in top-k processing.

January 2026: NVIDIA/NeMo-RL monthly summary focusing on stability and reliability improvements. Fixed a DTensor slicing crash introduced by PyTorch 2.9 changes, enhancing the stability of tensor operations for RL workloads and maintaining compatibility with the latest PyTorch release.

October 2025 monthly summary for NVIDIA/NeMo-RL: Delivered key on-policy distillation capabilities with emphasis on scalability, test coverage, and validation reliability. Implemented Megatron-based on-policy distillation for both student and teacher policies, enabling distributed training and improved performance. Refined on-policy distillation tests with tuned parameters across configurations, batch sizes, sequence lengths, and validation metrics to better cover diverse model configurations. These efforts improve training efficiency, scalability, and maintainability of the distillation workflow.

September 2025 — Delivered On-Policy Distillation for NeMo RL, introducing a KL-divergence loss-based student-teacher training workflow within the NeMo RL framework. The release includes configuration files, example scripts, and core training logic with distributed training support and generation backends such as vLLM. This work enhances scalability, enables efficient deployment of smaller, high-performing models, and accelerates experimentation for RL workloads. No major bugs reported this month, with a clear path for further improvements.