Tag: deep-learning

All the articles with the tag "deep-learning".

• From AlexNet to World Models: The Evolution of Multi-Modal Neural Networks

  2 Jun, 2026

  A ground-up tour of how neural networks learned to see, then to see-and-read, and finally to imagine. From AlexNet and CNNs, through CLIP and the vision-language models behind GPT-4V, to world models like Dreamer, V-JEPA 2, and LeWorldModel — with architectures, math, and benchmark numbers along the way.

• Attention Residuals: Softmax Attention Over Depth

  1 Jun, 2026

  A deep dive into the Kimi team's Attention Residuals (AttnRes) — replacing the fixed-weight residual connection with learned softmax attention over depth. Covers the time–depth duality, Full vs Block AttnRes, the structured-matrix view that unifies prior residual variants, the pipeline-parallel infra that makes it practical, and the scaling-law and 48B-MoE results.

• From GRPO to GSPO: Group-Based Policy Optimization for LLMs

  28 May, 2026

  A complete walkthrough of Group Relative Policy Optimization (GRPO) and Group Sequence Policy Optimization (GSPO) — the policy-gradient algorithms behind DeepSeek-R1 and Qwen3. Full math, the failure mode that motivated GSPO, the MoE story, and a side-by-side comparison.

• GRPO and Dr.GRPO: The Math, the Biases, and the Fix

  28 May, 2026

  An end-to-end derivation of Group Relative Policy Optimization (GRPO) from DeepSeekMath and the Dr.GRPO correction from Liu et al. Covers the full objective, the gradient, the two biases (length and question difficulty), the unbiased fix, and the practical recipe behind R1-Zero–style training.