JanusFlow introduces an innovative framework that integrates autoregressive language models with rectified flow, streamlining multimodal understanding and image generation. This minimalist architecture enhances performance while maintaining simplicity, allowing for superior results on standard benchmarks in both fields. Key strategies focus on decoupling encoders and aligning representations to improve training efficiency.
The DeepSeek-V3 model represents a significant leap in language model technology, featuring 671 billion parameters. This technical report outlines its innovative architecture, including Multi-head Latent Attention and an auxiliary-loss-free load balancing strategy, which optimize performance while minimizing training costs. Comprehensive benchmarks highlight its competitive advantages over existing models.
This document explores the innovative approaches taken with DeepSeek-R1, focusing on enhancing reasoning capabilities through large-scale reinforcement learning. The findings reveal how both DeepSeek-R1-Zero and DeepSeek-R1 models achieve remarkable performance in reasoning tasks by leveraging reinforcement learning strategies without relying on supervised methods, positioning these models at the forefront of AI advancements.
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