A New NVIDIA Research Shows Speculative Decoding in NeMo RL Achieves 1.8× Rollout Generation Speedup at 8B and Projects 2.5× End-to-End Speedup at 235B
The story
A new paper from NVIDIA Research integrates speculative decoding directly into NeMo RL with a vLLM backend, delivering lossless rollout acceleration at both 8B and projected 235B model scales. The post A New NVIDIA Research Shows Speculative Decoding in NeMo RL Achieves 1.8× Rollout Generation Speedup at 8B and Projects 2.5× End-to-End Speedup at 235B appeared first on MarkTechPost .
From the source
News Hub @media (max-width:767px){.tdi_8{margin-left:auto!important}} .tdb_mobile_search{margin-bottom:0;clear:none}.tdb_mobile_search a{display:inline-block!important;position:relative;text-align:center;color:var(--td_theme_color,#4db2ec)}.tdb_mobile_search a>span{display:flex;align-items:center;justify-content:center}.tdb_mobile_search svg{height:auto}.tdb_mobile_search svg,.tdb_mobile_search svg *{fill:var(--td_theme_color,#4db2ec)}#tdc-live-iframe .tdb_mobile_search a{pointer-events:none}.td-search-opened{overflow:hidden}.td-search-opened #td-outer-wrap{position:static}.td-search-opened .td-search-wrap-mob{position:fixed;height:calc(100% + 1px)}.td-search-opened .td-drop-down-search{height:calc(100% + 1px);overflow-y:scroll;overflow-x:hidden}.tdi_8{display:inline-block}.tdi_8 .tdb-head
The research team integrated speculative decoding directly into NeMo RL v0.6.0 with a vLLM backend, delivering lossless rollout acceleration at both 8B and projected 235B model scales.The latest NeMo RL v0.6.0 release officially ships speculative decoding as a supported feature alongside the SGLang backend, the Muon optimizer, and YaRN long-context training.
To understand the problem, it helps to know how a synchronous RL training step breaks down. In NeMo RL, each step consists of five stages : data loading, weight synchronization and backend preparation (prepare), rollout generation (gen), log-probability recomputation (logprob), and policy optimization (train).
Who and what
Key names and topics in this story: NVIDIA Research Shows Speculative, Decoding, NeMo RL Achieves, Rollout Generation Speedup.
Where to follow next
- Read the full piece at www.marktechpost.com
- More from our AI & prompts coverage
Related stories
Sakana AI Introduces KAME: A Tandem Speech-to-Speech Architecture That Injects LLM Knowledge in Real Time
Sakana AI Introduces KAME: A Tandem Architecture That Injects Real-Time LLM Knowledge Into Speech-to-Speech Conversational AI Without Adding Latency The post Sakana AI Introduces KAME: A Tandem Speech-to-Speech Architecture That Injects LLM Knowledge in Real Time appeared first o
What is Tokenization Drift and How to Fix It?
A model can behave perfectly one moment and degrade the next—without any change to your data, pipeline, or logic. The root cause often lies in something far more subtle: how your input is tokenized. Before a model processes text, it converts it into token IDs, and even minor form
A Coding Implementation to Parsing, Analyzing, Visualizing, and Fine-Tuning Agent Reasoning Traces Using the lambda/hermes-agent-reasoning-traces Dataset
In this tutorial, we explore the lambda/hermes-agent-reasoning-traces dataset to understand how agent-based models think, use tools, and generate responses across multi-turn conversations. We start by loading and inspecting the dataset, examining its structure, categories, and co
Study: AI models that consider user s feeling are more likely to make errors
Overtuning can cause models to "prioritize user satisfaction over truthfulness.”