EfficientRollout: System-Aware Self-Speculative Decoding for RL Rollouts
cs.LG
Submitted: 2026-06-17
Updated: 2026-09-30
Comments: Accepted to NeurIPS 2026; Project Page: https://github.com/furiosa-ai/EfficientRollout
Code: https://github.com/furiosa-ai/EfficientRollout
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
- Accelerating Large Language Model Decoding with Speculative Sampling
- Jackpot: Optimal Budgeted Rejection Sampling for Extreme Actor-Policy Mismatch Reinforcement Learning
- Multi-Head Attention: Collaborate Instead of Concatenate
- The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models
- The Llama 3 Herd of Models
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- History Rhymes: Accelerating LLM Reinforcement Learning with RhymeRL
- Measuring Mathematical Problem Solving With the MATH Dataset
- Accelerating RL Post-Training Rollouts via System-Integrated Speculative Decoding
- Revisiting Entropy in Reinforcement Learning for Large Reasoning Models
- Beyond Next-Token Prediction: A Performance Characterization of Diffusion versus Autoregressive Language Models
- LLM Post-Training: A Deep Dive into Reasoning Large Language Models
- DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
- SPEC-RL: Accelerating On-Policy Reinforcement Learning with Speculative Rollouts
- Understanding R1-Zero-Like Training: A Critical Perspective
- Proximal Policy Optimization Algorithms
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- Fast Transformer Decoding: One Write-Head is All You Need
- Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
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