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Octopus: One Brain, Many Branches for Latent-KV Multi-Agent Reinforcement Learning
Not provided in the abstract
multi-agentreinforcement learningKV cachingefficiency
2611.06956
Builder Relevance
3h ago80%
Abstract
Octopus introduces a multi-agent reinforcement learning framework that improves efficiency and accuracy by sharing a single model backbone and utilizing a directed acyclic graph for KV states.
Reality Card
Core Claim
Octopus achieves 49.6% accuracy on BrowseComp-Plus, outperforming single-agent GRPO by 17.5 points and dense-retrieval agents up to 250 times larger while increasing training throughput by 3.3 times.
Method / Result
Increased training throughput by 3.3 times and reduced peak KV memory by 2.8 times.
Limitations
The abstract does not provide specific details on the reproducibility of the results or the dataset used.
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