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KV-streams for Efficient Compaction in Agentic Reinforcement Learning

Paper recorded by Signals 4 on 2026-09-28 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-28 on arXiv · recorded by Signals 4 on 2026-09-29

Category: cs.AI · 人工智能 · first seen 2026-09-29

Abstract

Scaling the horizon of agentic LLMs is bottlenecked by the need to fit ever longer context traces in GPU memory. Context compaction has been the most popular mechanism to alleviate this issue, keeping GPU memory constant for a given trace. Unfortunately, most compaction strategies rely on prefilling the LLM context many times over, hindering training throughput. To alleviate this bottleneck and en

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#6 most recent of 440 cs.AI papers we have recorded · ↑ newer: How to Loop MoE: Flatten the Experts, Untie the Attention · ↓ older: Copy the Same, Distill the Difference: Initializing Linear Vision Tran

Cite this page: KV-streams for Efficient Compaction in Agentic Reinforcement Learning: the #6 most recent of 440 cs.AI papers we have recorded (as of 2026-09-28). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/kv-streams-for-efficient-compaction-in-agentic-reinforcement-learning.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json

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