Message Passing Enables Efficient Reasoning
While inference-time scaling has improved the reasoning abilities of large language models (LLMs), the need to generate long chains-of-thought (CoTs) is a computational bottleneck. Thus, in contrast to sequential scaling methods like CoT, recent parallel scaling techniques instead use fork and join (FJ) primitives to divide work across multiple LLM threads. However, in the fork-join paradigm, threads are typically transient and do not communicate pointwise with one another which limits scalability. To tackle this, we introduce Message Passing Language Models (MPLMs), a framework for LLM reason
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- LinkedLinked via unknownAdaptive Parallel Reasoning: The Next Paradigm in Efficient Inference Scaling →
- LinkedLinked via unknownNew benchmark exposes reasoning gaps in top models →
- LinkedLinked via unknownIEEE Rolls Out Large Language Models Virtual Training Course →
- LinkedLinked via unknownEvaluating long-term memory limits in stateless LLM chatbots — feedback needed [D] →
- LinkedLinked via unknownWould having a dedicated programming language specifically for LLMs be a viable solution? [D] →
- LinkedLinked via arxiv author · 85%Xuecheng Liu →
“Message Passing Enables Efficient Reasoning”
- LinkedLinked via arxiv author · 85%Daman Arora →
“Message Passing Enables Efficient Reasoning”
- LinkedLinked via arxiv author · 85%Gokul Swamy →
“Message Passing Enables Efficient Reasoning”