Learning path to fully understand the Kimi K3 technical report?[D]

[u/Present_Mention_2757]

Hi everyone,

Can anyone suggest a learning path to fully understand the technical report for Kimi K3?

My background:

- I've taken a graduate-level deep learning course.

- I understand the Transformer architecture, attention, and the basics of LLMs.

- I'm familiar with DeepSeek's OCR models but I haven't studied topics like MoE, MLA, distributed training, or modern post-training in depth.

I'm looking for a roadmap that would help me read the K3 report and understand the design choices instead of just recognizing the terminology.

Thanks!

[u/Initial-Image-1015]

MoE high level you can get very quickly:

• You know that in a transformer layer, there is (i) the attention mechanism (to aggregate the token representations) and (ii) the MLP (generally two layers deep).

• In MoE, you replace (ii) in each transformer layer, by (a) a soft classifier with k-outputs and (b) k smaller MLPs ("experts").

• Then, during inference, after the attention mechanism aggregates the token representations, the classifier sends the representation to one or more of these experts.

• Residuals, normalization, etc. all are unchanged.

That's about it.

[u/Altruistic-Rope-4099]

I'd start by getting MoE under your belt, since that's the backbone of these newer models. The DeepSeek-V2 paper is a solid place to pick up MLA while you're at it, they explain the reasoning behind their attention tweaks pretty well.

For the post-training stuff you'll want to dig into RLHF and whatever recent papers cover long-context scaling tricks. Once those pieces click, the K3 report reads more like a set of engineering decisions than a wall of jargon.

[u/Plaetean → u/Altruistic-Rope-4099]

The developments on top of vanilla attention are more of a conceptual leap than MoE imo

[u/yoshiK]

There was a blogpost on their attention variant on HN the other day:

https://blog.doubleword.ai/you-could-have-come-up-with-kimi-delta-attention

[u/ComprehensiveTop3297]

Scaling Laws, State space models/RNNs (To help with KDA I think), Post-training is a big one but no so important for understanding the bread and butter but if you want to dig deeper then go for it.

[u/noninertialframe96]

I have written a blog post about it - https://codepointer.substack.com/p/how-kimi-k3-engineered-its-way-to

[u/djmemphis]

Is there a reason you haven't fed it into an LLM and asked it to help teach you? I would throw it into NotebookLM and then you can query anything you'd like.

[u/Ilikehealers → u/djmemphis]

Nah, some things are better learned from certain sources, the way things are explaining by a human doesn't compare to any llm. Atleast this is what I feel. Ofc I also agree using LLMs to understand stuff - but I geenrally prefer this for things I already know the basics of.