LLM Research Trends: From Black-Box Distillation to From-Scratch Implementation
연구/벤치마크 | Mon Jun 29 2026 00:00:00 GMT+0000 (Coordinated Universal Time) | 3 sources
Recent LLM research and experimentation cases include the Proxy-KD technique, analysis of the tokenmaxxing phenomenon, and the from-scratch NanoEuler implementation.
Analysis
[Proxy-KD] proposed a new knowledge distillation technique for black-box LLMs [1]
- targets black-box teacher models like GPT-4
- overcomes internal state access limitations by leveraging a proxy model
- outperforms existing white-box KD techniques
[Tokenmaxxing] raised a reinterpretation of the intentionality behind enterprise token consumption mandates [2]
- case of Meta evaluating personnel based on token usage
- employees burning through tokens via meaningless agent conversations
- interpretation as intentional blunt force policy to break resistance to AI tool adoption
[NanoEuler] released a GPT-2 scale model implementation in pure C/CUDA without PyTorch [3]
- trains a ~116M parameter model on a single RTX 4070
- directly implements hand-written FlashAttention
- cuBLAS matmul
- and BPE tokenizer
- applies modern architecture elements like RMSNorm
- RoPE
- and SwiGLU
- validates end-to-end pipeline from pretrain to SFT