arXiv Artificial Intelligence

KV-streams for Efficient Compaction in Agentic Reinforcement Learning

1 hour ago 30.09.2026 07:00 👁 3 views
KV-streams for Efficient Compaction in Agentic Reinforcement Learning

Quick summary

arXiv:2609.35750v2 Announce Type: replace-cross 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 enable efficient trainable compaction, we propose KV-streams, a plug-and-play strategy compatible with any compaction strategy that substantiall

Key takeaways

  • arXiv:2609.35750v2 Announce Type: replace-cross 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.

Why it matters

AI progress is not only a software story. Chips, data centers and energy decisions help determine which models can operate economically and what end users ultimately pay.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗