Explaining LLM Concepts in 10 Words or Less
red-baton-ant · reddit · 2026-08-28
To better understand LLM internals, the author started explaining complex concepts in 10 words or less, maintaining a list on GitHub.
Examples include:
- Prefill: Process prompt tokens and populate KV cache.
- PagedAttention: Store KV cache in non-contiguous memory blocks.
- Continuous batching: Add new requests as existing requests finish.
- Speculative decoding: Draft with smaller model; verify with larger model.
- LoRA: Train low-rank weight updates instead of full weights.
- GRPO: Reinforce better answers within a group from the same prompt.
- GQA: One KV head shared by each query-head group.
The author welcomes feedback on technical accuracy and suggestions for missing concepts.
More from Research
- TTPO: Test-Time Policy Optimization for Label-Free Math Reasoning — Aozhe Wang · 2026-08-28
- Aphanta: Diagnosing Task-Aligned Image-Edited Intermediates — Fudan-University · 2026-08-28
- HydroGym: 60+ environments to train AI for fluid dynamics control — ricardovinuesa · 2026-08-28
- Miles now supports RL training for Qwen and GLM models with high-performance kernels — ying11231 · 2026-08-28
- Miles-diffusion introduces LoRA SFT for fast post-training of diffusion models — ying11231 · 2026-08-28
- SovietRxiv adds 7,000 translated Soviet scientific papers to archive — generativist · 2026-08-28