Records found: 7
Energy-Based Transformers enable machines to perform advanced, unsupervised System 2 Thinking, improving reasoning and generalization across tasks and modalities without domain-specific supervision.
Alibaba introduces Lumos-1, a unified autoregressive video generation model leveraging MM-RoPE and AR-DF to efficiently model spatiotemporal dependencies, achieving competitive results with efficient training.
MemAgent introduces a reinforcement learning-based memory agent that allows large language models to process ultra-long documents efficiently, maintaining high accuracy with linear computational costs.
DeepSeek researchers released nano-vLLM, a compact and efficient Python implementation of the vLLM engine that balances simplicity with performance for LLM inference.
New research demonstrates that inference-time prompting can effectively approximate fine-tuned transformer models, offering a resource-efficient approach to NLP tasks without retraining.
Meta and collaborators developed a novel framework to accurately quantify how much language models memorize training data, estimating GPT models store around 3.6 bits per parameter and providing new insights into memorization versus generalization.
PARSCALE introduces a parallel computation approach to scale language models efficiently, reducing memory use and latency while improving performance across various tasks.