BAIR

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High signal Matched: inference, decoding, prefill, generation, serve, throughput, kv cache, verification, performance, latency, cost, model, paper, research, evaluation, training, pretraining, sft, benchmarks, long context, context window, agentic, reasoning model

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--> Understanding the behavior of complex machine learning systems, particularly Large Language Models (LLMs), is a critical challenge in modern artificial intelligence. Interpretability research aims to make the decision-making process mo...

High signal Matched: inference, serving, decoding, performance, cost, model, research, training, evaluate, mmlu, long-context, rag

An encoder (optical system) maps objects to noiseless images, which noise corrupts into measurements. Our information estimator uses only these noisy measurements and a noise model to quantify how well measurements distinguish objects. Man...

High signal Matched: performance, model, paper, evaluation, training, evaluate

What exactly does word2vec learn, and how? Answering this question amounts to understanding representation learning in a minimal yet interesting language modeling task. Despite the fact that word2vec is a well-known precursor to modern lan...

High signal Matched: benchmark, performance, model, weights, paper, training

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Recent advances in Large Language Models (LLMs) enable exciting LLM-integrated applications. However, as LLMs have improved, so have the attacks against them. Prompt injection attack is listed as the #1 threat by OWASP to LLM-integrated ap...

High signal Matched: cost, model, evaluation, training, dpo, fine-tuning, retrieval, api, sota

PLAID is a multimodal generative model that simultaneously generates protein 1D sequence and 3D structure, by learning the latent space of protein folding models. The awarding of the 2024 Nobel Prize to AlphaFold2 marks an important moment...

High signal Matched: inference, generation, cost, model, weights, research, training, retrieval

In this post, I’ll introduce a reinforcement learning (RL) algorithm based on an “alternative” paradigm: divide and conquer. Unlike traditional methods, this algorithm is not based on temporal difference (TD) learning (which has scalabilit...

Watchlist Matched: benchmark, performance, model, paper, training

Training Diffusion Models with Reinforcement Learning We deployed 100 reinforcement learning (RL)-controlled cars into rush-hour highway traffic to smooth congestion and reduce fuel consumption for everyone. Our goal is to tackle "stop-and...

Watchlist Matched: throughput, kernel, performance, model, paper, training, agent, agents