Wen Zeng, Ke Hong, Xu Fu, Zixiao Huang, Zehao Wang, Zhuoran Zhu, Yuanqing Wang, Bo Dai 等 17 位作者
Embodied reinforcement learning (RL) coordinates policy inference, simulation, and model training with highly heterogeneous resource demands. Although individual components frequently underutilize GPU compute during execution, existing systems only overlap work at coarse component boundaries (e.g., pipelining inference and simulation across simulator groups), leaving substantial low-activity GPU windows within subcomponent phases and nested loops. Exploiting these fine-grained windows manually requires tedious dependency analysis and workload-specific coordination. Furthermore, even when independent work is scheduled concurrently, cross-process GPU time slicing and streaming multiprocessor (SM) contention can negate the benefits of overlap. We present Rose, a runtime-aware system that automates fine-grained overlap and GPU resource management for embodied RL while preserving synchronous training semantics. Through a lightweight programmable interface that exposes execution hierarchy and state accesses, Rose tracks runtime data readiness and GPU activity to perform a two-stage optimization. First, a window-guided graph optimizer identifies low-activity windows and applies graph transformations to overlap complementary work across components and loop levels. Second, leveraging NVIDIA Multi-Process Service (MPS) for cross-process GPU concurrency, a slack-aware resource manager tunes process-level SM quotas by exploiting branch synchronization slack to mitigate interference on the critical path. Both stages combine local profiling with end-to-end iteration validation to guarantee throughput gains with minimal tuning overhead. Across diverse policies, simulators, RL algorithms, and GPU platforms, Rose achieves 1.20--1.92$\times$ training throughput improvement and increases average SM activity by 23.8--39.3\% compared with the state-of-the-art systems.
Bowen Zeng, Feiyang Ren, Jun Zhang Qwen, Xiaoling Gu Hangzhou Dianzi University, Ke Chen Zhejiang University, Lidan Shou Zhejiang University, Huan Li Zhejiang University
Multimodal Large Language Models (MLLMs) have advanced unified reasoning over text, images, and videos, but their inference is hindered by the rapid growth of key–value (KV) caches. Each visual input expands into thousands of tokens, causing caches to scale linearly with context length and remain resident in GPU memory throughout decoding, which leads to prohibitive memory overhead and latency even on high-end GPUs. A common solution is to compress caches under a fixed allocated budget at different granularities: token-level uniformly discards less important tokens, layer-level varies retention across layers, and head-level redistributes budgets across heads. Yet these approaches stop at allocation and overlook the heterogeneous behaviors of attention heads that require distinct compression strategies. We propose HybridKV, a hybrid KV cache compression framework that integrates complementary strategies in three stages: heads are first classified into static or dynamic types using text-centric attention; then a top-down budget allocation scheme hierarchically assigns KV budgets; finally, static heads are compressed by text-prior pruning and dynamic heads by chunk-wise retrieval. Experiments on 11 multimodal benchmarks with Qwen2.5-VL-7B show that HybridKV reduces KV cache memory by up to 7.9\times and achieves 1.52\times faster decoding, with almost no performance drop or even higher relative to the full-cache MLLM.
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cs.CL · cs.CV
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表