1 citations · 1 across the 4 of their papers we have counts for
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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…
The challenge of hidden gifts in multi-agent reinforcement learning
Dane Malenfant, Blake A. Richards
Sometimes we benefit from actions that others have taken even when we are unaware that they took those actions. For example, if your neighbor chooses not to take a parking spot in…
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RL
Chen Sun, Wannan Yang, Thomas Jiralerspong +4
In real life, success is often contingent upon multiple critical steps that are distant in time from each other and from the final reward. These critical steps are challenging to i…