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Mesh LLM: distributed AI computing on iroh

AI·The iroh.computer blog introduces Mesh LLM, a distributed AI computing system running on the iroh network protocol.

Can Dialects Be Steered Like Languages? Sparse Neurons and Distributed Directions in Arabic LLMs

A key challenge in Arabic NLP is the scarcity of dialectal data relative to Modern Standard Arabic (MSA), causing LLMs to overproduce MSA and struggle with dialectally accurate generation. From an interpretability perspective, this raises a fundamental question: where and how are dialectal features encoded within model internals, and can these representations be leveraged to improve dialect generation without fine-tuning? This study investigates two complementary inference-time approaches that …

Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training

Reinforcement learning (RL) has become a central component of post-training large language models (LLMs), yet little is understood about how RL adaptation is distributed across transformer layers. Existing approaches typically update all model parameters uniformly, implicitly assuming that every layer contributes similarly to the gains obtained during RL post-training. In this work, we challenge this assumption through a systematic layer-wise study of RL training. Surprisingly, we find that tra…