Hugging Face Trending Papers
TailSieve: Partial-Rollout-Guided Tail Routing for LLM Rollouts
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
arXiv AI
Aug 25
TailSieve: Partial-Rollout-Guided Tail Routing for LLM Rollouts
arXiv:2608.22788v1 Announce Type: new Abstract: Large-scale rollouts have become a core component of modern LLM systems, spanning reinforcement learning (RL) post-training, on-policy distillation (OP...
arXiv Machine Learning
Jun 18
EfficientRollout: System-Aware Self-Speculative Decoding for RL Rollouts
arXiv:2606. 18967v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a representative post-training paradigm for LLMs, enabling strong reasoning and agentic capabilities.
arXiv Machine Learning
Aug 14
RoutePack: Expert Placement and Attention-Aware Data Packing for MoE Reinforcement Learning
arXiv:2608. 12146v1 Announce Type: cross Abstract: Training Mixture-of-Experts (MoE) models for reinforcement learning (RL) couples two load-balancing problems: sequence composition determines dense attention work in each data-parallel microbatch, while token routing determines sparse expert work on expert-parallel ranks.
arXiv AI
Jul 7
DSpark: Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generation
arXiv:2607. 05147v1 Announce Type: new Abstract: Speculative decoding accelerates Large Language Model (LLM) inference by decoupling draft generation from target verification.
arXiv AI
Jul 21
WAR: Workload-Aware Rollouts for Synchronous Agentic Reinforcement Learning
arXiv:2607. 17299v1 Announce Type: cross Abstract: Long-horizon rollout generation has become the dominant systems bottleneck in agentic reinforcement learning (RL).
Hugging Face Trending Papers
Jul 6
Adaptive Inference Batching using Policy Gradients
Inference serving systems must balance throughput and latency under bursty, heterogeneous workloads, yet the industry standard remains static batching policies that require manual tuning and cannot adapt to shifting traffic. We investigate whether reinforcement learning (RL) can learn adaptive batching and routing policies that outperform these heuristics, training REINFORCE and PPO agents on a discrete-event simulator validated against queuing theory and production traces (Azure Functions, BurstGPT).