4 citations · 11 across the 11 of their papers we have counts for
Showing 2026Show all
2 papers · 1 filter
cs.LG2026
KV-streams for Efficient Compaction in Agentic Reinforcement Learning
Emiliano Penaloza, Dane Malenfant, Dheeraj Vattikonda +15
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…
cs.LG2026
Le Critique: Privileged Value Functions for LLM Reinforcement Learning
Siddarth Venkatraman, Matthieu Dinot, Laurence Aitchison
Reinforcement learning algorithms for Large Language Models (LLMs) are largely distinguished by their variance reduction strategy. Group-relative methods like GRPO reduce gradient…