AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Training large language models (LLMs) across geographically distributed and poorly interconnected compute pools or data centers has become a topic of significant interest. This has led to increased study of local optimization methods such as DiLoco for LLMs, especially those that use local (or inner) adaptive or precon...
Diffusion language models are a promising and more efficient alternative to autoregressive models. However, most RL post-training techniques are not easily adapted to these models. One issue is that diffusion language models generate answers through many intermediate states, deciding at each step which masked positions...
This work pretraining three 3B block-diffusion denoisers (attention, mamba, and hybrid) on 300B tokens under one single-frontier objective and decoding all three through a single cached interface finds the Mamba cache delivers 4.3x lower latency, 11x less memory, and 2.6x higher single-stream throughput.
Efficient Long-hOrizon (ELO) learning is introduced, an efficient meta-training algorithm that reallocates wasted meta-training compute to longer failure regimes, achieving efficient long-horizon learning, and enforces decoupled progressive expert supervision, providing stable meta-learning signals that additionally im...
A mechanistic analysis of the jailbreaking behavior in a large-scale, safety-aligned LLM, focusing on LLaMA-2-7B-chat-hf identifies computational circuits responsible for generating affirmative responses to jailbreak prompts and uncovers key attention heads and MLP pathways that mediate adversarial prompt exploitation.
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