Chain-of-thought (CoT) reasoning allows humans to inspect how large language models reach their answers, and oversee model behaviour. This reasoning comes at an increased inference cost, motivating efficient methods that train models to solve tasks using fewer tokens. However, a common concern is that such training may...
Samuel Lewis-Lim, Xing-Wei Tan, Mario Sanger et al.· 0 citations
This work proposes BusMA, a communication framework that allows any agent to address other agents through a shared channel, i.e., the Bus, which consists of agent registration, message routing, and shared memory management components.
Yan-Wen Peng, Delvin Ce Zhang, Xi Wang et al.· 0 citations
This work proposes StateBridge, a training-free latent communication approach that aligns the sender's final-layer hidden states to the receiver's input space via a closed-form orthogonal transformation and prepended to the input of the receiver agent as a continuous prefix.
Yan-Wen Peng, Delvin Ce Zhang, Xi Wang et al.· 6 citations
Calculating the evaluation to a 100B-token regime, logic pre-pretraining substantially accelerates skill acquisition in LMs, achieving 80\% accuracy on linguistic tasks with 36B fewer tokens than standard initialization, and outperforming alternative pre-pretraining baselines.
Jo-Ku Cheng, Nikolaos Aletras, M. Valentino· 0 citations
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