wehub-resource-syncARIS Tutorials
Long-form interview-prep cheat sheets, written in Markdown and rendered to single-file HTML via the /render-html skill (academic-newspaper template, sticky TOC, MathJax + highlight.js, cross-model codex review gate).
📖 Curated collection: github.com/wanshuiyin/ARIS-in-AI-Offer — interview-prep cheat sheets organized into 6 categories with bilingual README.
🧠 General / Foundations
| Tutorial | MD | HTML | Topics |
|---|---|---|---|
| Attention 面试 Cheat Sheet | md | html | Scaled-dot-product, MHA / MQA / GQA, RoPE / ALiBi, FlashAttention, KV cache, attention in diffusion, NaN-mask trap |
| KL Divergence in RLHF | md | html | k1/k2/k3 estimators · forward vs reverse KL · KL in PPO/GRPO/DPO · placement gradient bias · "Rethinking KL" + "Comedy of Estimators" |
🎯 Post-Training & Reasoning
| Tutorial | MD | HTML | Topics |
|---|---|---|---|
| RLHF / DPO / GRPO / PPO | md | html | PPO clip + GAE · RLHF pipeline · DPO closed-form from BT · GRPO group-relative · KTO/IPO/SimPO/ORPO · PRM vs ORM · Constitutional AI |
| Reasoning Models (o1 / R1 / Test-Time Compute / PRM) | md | html | o1/o3/R1 three-way comparison · GRPO derivation · PRM vs ORM · s1 budget forcing · MCTS+PUCT · R1-Distill |
| LLM On-Policy Distillation (OPD) | md | html | Route A (full-vocab) vs Route B (REINFORCE/IS, Tinker default) · vOPD control variate · OPD+GRPO · MiniLLM / GKD / Qwen3 / Thinking Machines |
🏛️ LLM Architecture & Systems
| Tutorial | MD | HTML | Topics |
|---|---|---|---|
| MoE (Mixture-of-Experts) | md | html | DeepSeek-V3 fine-grained + shared · Mixtral · Llama 4 · auxiliary-loss-free balancing · EP all-to-all · DualPipe · capacity factor |
| Long Context (RoPE / YaRN / NTK / MLA / StreamingLLM) | md | html | RoPE rotation, PI/NTK/YaRN/LongRoPE scaling, MLA decoupled RoPE, SWA + StreamingLLM, Ring Attention |
| KV Cache + Speculative Decoding | md | html | PagedAttention, MQA/GQA/MLA, speculative decoding acceptance prob, Medusa / EAGLE-1/2/3, Lookahead |
| Quantization | md | html | GPTQ Hessian-based · AWQ activation-aware · SmoothQuant · LLM.int8 · QuaRot/SpinQuant · FP8 E4M3/E5M2 · MX formats · NVFP4 |
| Distributed Training | md | html | DDP / FSDP2 / ZeRO 1/2/3 + ZeRO++ / TP (Megatron) / PP (GPipe, 1F1B, interleaved) / SP / CP / EP / DualPipe / Llama 3 |
🌊 Generative Models — Theory & Tokenizers
| Tutorial | MD | HTML | Topics |
|---|---|---|---|
| Flow Matching Quick Reference | md | html | Conditional FM, Rectified Flow / VP / VE paths, training + sampling code, ODE solvers, SD3 / FLUX latent FM |
| Diffusion Foundations (DDPM / Score / DDIM / EDM / CFG) | md | html | DDPM ELBO + L_simple, score matching + Tweedie, Score SDE + PF-ODE, DDIM, EDM preconditioning + Heun, CFG, Consistency Models + LCM + Turbo |
| VAE / VQ-VAE / VQ-GAN / FSQ | md | html | VAE ELBO + reparam · β-VAE · IWAE · posterior collapse · VQ-VAE STE + EMA codebook · VQ-GAN + PatchGAN · FSQ even/odd levels · LFQ |
🎨 Generation Systems
| Tutorial | MD | HTML | Topics |
|---|---|---|---|
| Image Generation Systems | md | html | LDM · SD/SDXL/SD3/FLUX · DiT · AdaLN-Zero · ControlNet · IP-Adapter · LoRA · DreamBooth · ADD/LADD distillation |
| Video Generation | md | html | 3D Causal VAE · Spacetime Patches · Spatiotemporal Attention · MM-DiT · I2V · VBench · Sora / Hunyuan-Video / Wan |
| 3D Generation | md | html | NeRF volumetric rendering · Instant-NGP hash · 3DGS rasterization · SDS / VSD · Trellis / Hunyuan3D |
| Diffusion Post-Training | md | html | DDPO · DPOK · DRaFT-K · AlignProp · Diffusion-DPO · D3PO · SPO · Diffusion-KTO · MaPO · Flow-GRPO |
| Diffusion / Flow Distillation | md | html | CM · iCT · sCM · CTM · LCM/TCD · rCM · DMD/DMD2 · ADD/LADD/Lightning · Rectified Flow/InstaFlow · Progressive distillation |
👁️ Multimodal
| Tutorial | MD | HTML | Topics |
|---|---|---|---|
| VLM (CLIP / LLaVA / Qwen-VL / DeepSeek-VL) | md | html | CLIP InfoNCE derivation, SigLIP, ViT, BLIP-2 Q-Former, Flamingo Perceiver, LLaVA, Qwen2-VL M-RoPE |
✅ 23 tutorials across 6 categories; each also available in English alongside the Chinese version (e.g.
attention_tutorial_en.md/_en.html). Full curated collection: ARIS-in-AI-Offer.
How they were produced
The two pilots were drafted by hand and rendered via /render-html. Subsequent tutorials use the dedicated workflow skill:
/interview-cheatsheet "<TOPIC>" # default: 600-line balanced effort
/interview-cheatsheet "<TOPIC>" — effort: max # ~1000 lines + deeper proofs
/interview-cheatsheet (skills/interview-cheatsheet/SKILL.md) is an ARIS skill that:
- Plans a 12-14 section structure (TL;DR · intuition · formula+derivation · from-scratch PyTorch · variants · 25 高频面试题 L1/L2/L3)
- Drafts the MD following the canonical style of the two pilot tutorials (heading conventions, table-pipe escapes, callout-list separation rules — all bugs caught during the pilot reviews are now encoded into the style guide)
- Cross-model
codex gpt-5.5 xhighreview on math / code / interview-answer / citation correctness + personal-info redaction (fresh thread, nevercodex-reply) - Fix-and-loop — trajectory-based (no hard cap; stop if same issue recurs or ~6 rounds without convergence)
- Renders via
/render-html(which itself runs a 13-check codex review on the rendered output) - Writes a combined audit trail to
*.review.json - Stops — never auto-commits. The user reviews and pushes manually.
See
skills/interview-cheatsheet/SKILL.mdfor the full skill protocol andskills/render-html/SKILL.mdfor the renderer.