December 2025 monthly summary for volcengine/verl: Delivered SpecRL, a model-free speculative decoding method to accelerate RL rollout, achieving up to 2.1x speedup by reusing historical response segments as drafts. Integrated end-to-end into Verl training workflow, enabling default speculative decoding and removing drafting costs while maintaining training stability. Validated across multiple backends (Qwen3-14B, Qwen2.5 family) with rollout.n=5, demonstrating robust throughput gains and reliable convergence. No major bugs fixed this month; emphasis on feature delivery, code quality, and CI/test readiness. Impact: faster experimentation cycles, reduced per-epoch compute, and a stronger foundation for scaling RL in production. Technologies/skills demonstrated: reinforcement learning pipelines, speculative decoding, large-model experimentation, distributed training orchestration (Ray trainer, FSDP), and cross-model validation.

Month: 2025-08 — Focused on reliability and correctness in token sampling for ROCm/vllm. Key outcomes include a thread-safety data race fix in the sample_recovered_tokens_kernel, improved production stability, and a clean commit with sign-off.