Multi-Stream LLMs: A Parallel Architecture to Overcome Single-Stream Bottlenecks in Language Models
By
atomicthumbs
Toasted just enough. A reliable bake, gently seasoned.
Summary
This paper introduces "Multi-Stream LLMs," a novel approach to overcoming the limitations of current language model architectures that rely on single-stream, sequential message exchanges. The authors identify key bottlenecks: agents cannot generate output while reading, cannot react to new information while writing, and cannot think while acting. They propose switching from instruction-tuning for sequential message formats to instruction-tuning for multiple parallel streams of computation, where each role (user, system, tool, self) gets its own stream. This allows models to simultaneously read from multiple input streams and generate tokens in multiple output streams. The approach promises to improve efficiency through parallelization, enhance security through better separation of concerns, and improve model monitorability.
Key quotes
· 5 pulledThis bottleneck to a single stream in chat models leads to a number of limitations: the agent cannot act (generate output) while reading, and in reverse, cannot react to new information while writing.
Similarly, the agent cannot act while thinking and cannot think while reading or acting on information.
In this work, we show that models can be unblocked by switching from instruction-tuning for sequential message formats to instruction-tuning for multiple, parallel streams of computation, splitting each role into a separate stream.
Every forward pass of the language model then simultaneously reads from multiple input streams and generates tokens in multiple output streams, all of which causally depend on earlier timesteps.
We argue that this data-driven change remedies a number of usability limitations as outlined above, improves model efficiency through parallelization, improves model security through better separation of concerns and can further improve model monitorability.
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