inference latency
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End-to-End Test-Time Training for Long Context
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Long-context language modeling is approached as a continual learning problem, utilizing a standard Transformer architecture with sliding-window attention. The model continues to learn during test time by predicting the next token based on the given context, effectively compressing the context into its weights. By employing meta-learning during training, the model's initialization is enhanced for learning at test time. This End-to-End Test-Time Training (TTT-E2E) method demonstrates scalability similar to full attention Transformers while maintaining constant inference latency, offering a significant speed advantage. This development is crucial as it provides a more efficient approach to handling long-context language tasks, improving both performance and speed.
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Hierarchical LLM Decoding for Efficiency
Read Full Article: Hierarchical LLM Decoding for Efficiency
The proposal suggests a hierarchical decoding architecture for language models, where smaller models handle most token generation, while larger models intervene only when necessary. This approach aims to reduce latency, energy consumption, and costs associated with using large models for every token, by having them act as supervisors that monitor for errors or critical reasoning steps. The system could involve a Mixture-of-Experts (MoE) architecture, where a gating mechanism determines when the large model should step in. This method promises lower inference latency, reduced energy consumption, and a better cost-quality tradeoff while maintaining reasoning quality. It raises questions about the best signals for intervention and how to prevent over-reliance on the larger model. This matters because it offers a more efficient way to scale language models without compromising performance on reasoning tasks.