arXiv Machine Learning June 16, 2026 By Jiarui Zhang, Yuchen Yang, Ran Yan, Zhiyu Mei, Liyuan Zhang, Daifeng Li, Wei Fu, Jiaxuan Gao, Shusheng Xu, Yi Wu, Binhang Yuan

AREAL-DTA: Dynamic Tree Attention for Efficient Reinforcement Learning of Large Language Models

Read the original on arXiv Machine Learning →

arXiv:2602. 00482v2 Announce Type: replace Abstract: Reinforcement learning (RL)-based post-training for large language models (LLMs) is computationally expensive, as it generates many rollout sequences that frequently share long token prefixes.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Aug 31

HARTS: Efficient Agentic Reinforcement Learning for Hybrid-Attention Models over Arbitrary Rollout Trees

HARTS (Hybrid‑Attention RL over Tree Structures) is a new system that jointly plans microbatches, data‑parallel replica assignments, and microbatch‑slot schedules to efficiently train agentic reinforcement learning models with hybrid attention over arbitrary rollout trees. It uses prefix compression and a linear‑time algorithm for chunkwise linear attention to avoid recomputing shared prefixes, enabling activation recomputation and bounded state replay while preserving trajectory‑wise training benefits. In experiments on an Agentic RL workload derived from SWE‑bench tasks, HARTS delivers 4.81–4.87× speedups in forward, backward, and gradient computations across multiple parallel configurations, with numerical differences comparable to baseline self‑rerun variation and a similar reward trend over the first 120 training steps.

By Boyuan Meng (Ant Group, China), Peihua Bao (Ant Group, China), Hong Liu (Ant Group, China), Xiaowei Zhu (Ant Group, China), Chao Wang (Ant Group, China), Gen Li (Ant Group, China), Zhenxuan Pan (Ant Group, China)

agents reinforcement-learning safety

Hugging Face Trending Papers

Jun 3

Read the Trace, Steer the Path: Trajectory-Aware Reinforcement Learning for Diffusion Language Models

Diffusion large language models (dLLMs) generate responses by iteratively unmasking and revising many positions in parallel. This process leaves a rich denoising trace depicting which tokens become confident, which remain unstable, and when commitments form.

llms diffusion reinforcement-learning

arXiv AI

Sep 1

When Do Larger Batches Help Scale LLM Reinforcement Learning?

arXiv:2608.29296v1 Announce Type: cross Abstract: Larger batches reduce the variance of stochastic gradients per update and are therefore often expected to accelerate training. Yet whether this stati...

By Ziniu Li, Jinbo Wang, Guanhua Huang, Feiyuan Zhang, Pengbo Li, Alex Chen

llms reinforcement-learning efficiency

arXiv AI

Aug 11

DistillCache: KL-Guided Adaptive KV-Cache Eviction for Memory-Efficient LLM Inference

arXiv:2608. 08878v1 Announce Type: cross Abstract: Transformer-based large language models (LLMs) achieve strong performance across many tasks, but their Key-Value (KV) cache grows linearly with sequence length, creating a severe memory bottleneck for long-context inference.

By Asaad Althoubi

llms reinforcement-learning efficiency benchmarks

arXiv Machine Learning

Jul 17

Branching Policy Optimization: Sandbox-Native Language Agent Reinforcement Learning

arXiv:2607. 14171v1 Announce Type: new Abstract: Reinforcement learning has emerged as the dominant paradigm for training large language model (LLM) agents that interact with executable sandboxes.

By Bowei He, Yankai Chen, Xiaokun Zhang, Xue Liu

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Jul 7

Learning When to Attend: Conditional Memory Access for Long-Context LLMs

arXiv:2603. 17484v2 Announce Type: replace-cross Abstract: Language models struggle to generalize beyond pretraining context lengths, limiting long-horizon reasoning and retrieval.

By Sakshi Choudhary, Aditya Chattopadhyay, Luca Zancato, Elvis Nunez, Matthew Trager, Wei Xia, Stefano Soatto

llms efficiency

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Related stories

HARTS: Efficient Agentic Reinforcement Learning for Hybrid-Attention Models over Arbitrary Rollout Trees

Read the Trace, Steer the Path: Trajectory-Aware Reinforcement Learning for Diffusion Language Models

When Do Larger Batches Help Scale LLM Reinforcement Learning?

DistillCache: KL-Guided Adaptive KV-Cache Eviction for Memory-Efficient LLM Inference

Branching Policy Optimization: Sandbox-Native Language Agent Reinforcement Learning

Learning When to Attend: Conditional Memory Access for Long-Context LLMs