arXiv:2606. 24369v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a dominant post-training paradigm, driving the emergence of high-performance RL systems such as veRL for autoregressive large language models (LLMs).
Reinforcement learning (RL) has become a dominant post-training paradigm, driving the emergence of high-performance RL systems such as veRL for autoregressive large language models (LLMs). In parallel, diffusion-oriented RL algorithms, e.
LeanGRPO eliminates redundant recomputation in diffusion reinforcement learning by reusing computation graphs and activations from rollout for policy updates, or by backpropagating provisional gradients and correcting them later. It introduces two training schedules—LeanGRPO‑Retain and LeanGRPO‑Reweight—that target different model scales and input sizes. Experiments on FlowGRPO/DanceGRPO with FLUX.1‑dev and Wan show up to a 1.83× end‑to‑end speedup while preserving the original optimization objective.
LeanGRPO eliminates redundant recomputation in diffusion reinforcement learning by reusing the same feed-forward backbone for rollout and policy update, thereby avoiding unnecessary gradient tracking. It introduces two training schedules—LeanGRPO‑Retain, which reuses computation graphs and activations, and LeanGRPO‑Reweight, which backpropagates provisional gradients and corrects them later. These methods achieve up to a 1.83× speedup on FlowGRPO/DanceGRPO with FLUX.1‑dev and Wan while preserving the original optimization objective.
arXiv:2604. 26256v2 Announce Type: replace Abstract: Reinforcement learning (RL) has become a critical paradigm for LLM post-training, yet the rollout phase -- accounting for 50--80% of total step time -- is bottlenecked by skewed generation: long-tailed trajectories indispensable for model performance block the entire training pipeline.
arXiv:2606. 19004v1 Announce Type: cross Abstract: Reinforcement learning (RL) post-training of Diffusion Transformers (DiTs) is prohibitively expensive, requiring thousands of high-end GPUs.
arXiv:2607. 13394v1 Announce Type: cross Abstract: Generative Flow Networks (GFlowNets) offer a promising alternative to reward-maximizing reinforcement learning (RL) for large reasoning models, encouraging diverse reasoning paths by matching reward distributions rather than collapsing to dominant modes.
arXiv:2512. 22560v2 Announce Type: replace-cross Abstract: Agentic Reinforcement Learning (RL) trains LLMs through multi-turn interactions with environments, producing workloads that mix compute-bound prefill, bandwidth-bound decoding, CPU-heavy environment execution, and bursty reward evaluation.
arXiv:2606. 03077v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a standard post-training paradigm for large language models (LLMs), extending beyond preference alignment to complex reasoning and multi-turn agentic behaviors.
The paper examines the computational challenges of training Reasoning Language Models (RLMs) using reinforcement learning with verifiable rewards (RLVR) and similar post‑training methods. It provides a compute‑centric analysis of popular RL algorithms such as PPO and GRPO, and introduces a taxonomy of intra‑ and inter‑model parallelism strategies—including traditional and novel techniques—to improve scalability and cost‑efficiency. The authors also evaluate existing RLM frameworks and offer practical guidelines and research directions for building high‑performance, scalable RLMs.
The paper "Performance Foundations of Parallel & Distributed Reasoning Language Models" examines how reinforcement learning with verifiable rewards (RLVR) and similar post‑training methods improve reasoning in large language models, yet demand massive computational resources. It provides a compute‑centric analysis of key RL frameworks such as PPO and GRPO, and introduces a taxonomy of intra‑ and inter‑model parallelism strategies—including data, tensor, pipeline, sequence, context, expert, disaggregated placement, stage fusion, hybrid parallelism, and asynchronous execution—to address the parallel and distributed systems challenges of training reasoning language models. The authors also analyze existing RLM frameworks, offering practical guidelines and outlining open research directions for building scalable, fast, and cost‑effective RLMs.
arXiv:2606. 26997v1 Announce Type: cross Abstract: Large language model (LLM) post-training for reasoning increasingly relies on reinforcement learning with verifiable rewards (RLVR), where models learn from ground-truth feedback on mathematical, logical, and scientific tasks.
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.