The Flow has not summarised this story yet — read it at arXiv Machine Learning.
TableTune is a system that automatically measures and corrects draft-length tables for speculative decoding in large language model serving. It profiles decoding performance across batch sizes and draft lengths, generates an optimized table in minutes, and a controller plugin continuously updates the table based on live traffic. Experiments on H100 GPUs show that TableTune consistently achieves at least 94% of the best fixed draft-length throughput, outperforms other speculative control methods, and can recover up to 96% of performance lost by stale tables, delivering significant speedups in real deployments.
ScaleRLT introduces a hybrid learner log‑probability method that reconciles differences between dense and deferred rollout computations in looped language models like Ouro. By using a stop‑gradient replay of the rollout schedule, the approach achieves bit‑for‑bit reproducibility across FP32 and bf16 real‑weight cells while maintaining the same gradient accuracy as dense implementations. The technique boosts rollout throughput by up to 1.56× on a single GPU and significantly speeds up multi‑node GRPO updates, leading to substantial accuracy gains on GSM8K in short training runs.
The paper introduces Temperon, a training strategy that uses plain SGD for the first 43% of the epoch budget and then hands off to a SAM‑wrapped Muon refiner for the remaining training. On datasets such as CIFAR‑10/100, SVHN, and Tiny ImageNet, Temperon achieves the same or better accuracy as full‑time SAM while reaching key performance targets faster and at lower cost. Ablation studies show that the Muon refiner contributes the majority of the performance gain, while the initial SGD explorer and its restarts add negligible benefit.
The paper presents an end‑to‑end MXFP8 reinforcement learning implementation on Blackwell GPUs, evaluating its benefits and limitations with Qwen3-8B and Qwen3-30B-A3B models. It finds that matching FP8 precision between sampler and learner improves consistency, that speed gains appear only for large micro‑batches, and that several weight‑synchronization correctness conditions can fail silently. The study also introduces seven explicit checks to detect such failures during training.
arXiv:2607. 20792v1 Announce Type: new Abstract: Memoir combines per-sample fast memory, shared slow parameters, variable-depth latent recurrence, and a future-latent energy objective.
SCLATE is a new execution substrate that allows continual‑learning benchmarks and agents to share a single event scheduler via adapters, enabling tasks, session events, and memory consolidation to run on a compressed, real‑time timeline. It also functions as a rollout engine that records every model call’s tokens and log probabilities without modifying the agent’s harness or memory. Using SCLATE, the authors ported seven benchmarks, compared ten harness‑memory configurations across ten models, and demonstrated that post‑training Qwen3.5‑4B can effectively leverage both harness and memory, improving performance on multiple metrics.