June 2026 monthly summary for NVIDIA/NeMo-RL. Delivered extended model support to enable end-to-end RL training with new model families, reinforced with robust testing and integration work. Focused on business value by expanding model coverage, stabilizing training workflows, and reducing time to experiment with Gemma4 and Mistral Medium 3.5. Overview of delivery: - Implemented Gemma 4 model support and Mistral Medium 3.5 (128B) text-only DAPO support in NeMo-RL, including model-specific training recipes, architecture workarounds, config handling, and vLLM integration. - Built automated test suites to validate compatibility and correct handling of architecture-specific requirements for Gemma4 and Mistral 3.5. - Delivered checkpoint loading and KV caching workarounds to ensure stable training across new architectures and improved performance. - Updated configuration paths and runtime plumbing to support the new models within the NeMo-RL training ecosystem. - Strengthened end-to-end pipeline readiness for production experiments with Gemma4 and Mistral 3.5. Impact: - Business value: Broader model support enables more customers to run advanced RL training experiments, accelerating experimentation cycles and time-to-insight. - Technical impact: Improved training reliability, reproducibility, and performance for Gemma4 and Mistral 3.5, with automated validation and reduced onboarding friction for ML researchers. Technologies/skills demonstrated: - NeMo-RL model integration, vLLM, and training recipe design. - Architecture-specific handling, checkpoint loading, and KV caching optimization. - Test automation, CI/test coverage, and configuration management. - Collaboration across model families and co-authors to land features.

Monthly summary for 2026-05 focusing on NVIDIA-NeMo/Automodel: delivered a targeted fix to clamp shared-expert SwiGLU activations in fp32 to align with the DSV4 reference, preventing runaway activations and ensuring reproducible MLP behavior. Implemented optional swiglu_limit wiring through the MoE config so the gated path uses controlled clamping and casts back before down-projection, mirroring the reference implementation. Prepared and documented upstream-ready changes including alignment with HF commit 2b2bebc, while preserving existing behavior for non-DSV4 callers (default swiglu_limit=0.0).

April 2026 (2026-04) focused on refining routing accuracy in the Gemma4 MoE decoder within NVIDIA-NeMo/Automodel, delivering a critical bug fix, associated tests, and measurable improvements in model routing and stability. The work enhances model reliability and performance for production-grade deployment.

December 2025 monthly summary for NVIDIA/NeMo-RL focusing on performance and efficiency improvements through FP8 KV-cache quantization. Implemented FP8 KV-cache quantization support with robust configuration, compatibility checks, and calibration for Q/K/V scales, enabling faster training and inference and reduced memory footprint.,”

September 2025 monthly summary for volcengine/verl: focused on delivering Truncated Importance Sampling (TIS) support in the Megatron backend for the Verl actor, with code changes committed and prepared for evaluation. The work enhances sampling configurability and reinforcement learning performance, and demonstrates strong cross-module collaboration and code-quality practices.