Hot Braise Analogy Corrected

Hot Braise Analogy Corrected | Generated by AI

Home 2026.07

Question: Is the analogy “热卤 = model compile, 先热后卤 = 先编译后训练” correct?

Answer:

The analogy is interesting, but not exactly correct. It mixes up food preparation order with ML system stages.

A better mapping:

Food	ML
Ingredients (鸭翅、鸭心、鱼蛋)	Raw data / model architecture / weights
熬卤汁 (prepare flavor base)	Build training infrastructure / optimizer / tokenizer / dataset pipeline
卤 (infuse flavor into ingredients)	Training / fine-tuning (weights learn patterns)
热卤成品	A trained model ready for inference
加热复用老卤	Inference / serving with existing weights

So:

ingredients
    ↓
prepare brine
    ↓
braise/infuse
    ↓
finished dish

is closer to:

raw data + architecture
    ↓
training setup
    ↓
gradient descent
    ↓
trained weights

“热卤大模型” if we interpret it technically:

The closest AI concept is probably model compilation / optimization, not training.

A modern LLM deployment pipeline:

PyTorch model
    |
    | compile
    ↓
TensorRT / vLLM / llama.cpp / XLA
    |
    ↓
optimized inference engine

Compilation changes how the model runs, not what the model knows.

Example:

Before compile:

GPT weights:
W1, W2, W3...

PyTorch graph:
matmul → attention → softmax → matmul

After compile:

optimized kernel:
fused_attention_kernel()
fused_mlp_kernel()

The model knowledge is unchanged.

So:

compile ≠ training

A compiled bad model is still a bad model.

“先热后卤” analogy:

If you mean:

first compile, then train

Usually wrong.

Training happens first:

Define model
Train
Save weights
Compile for inference
Serve

Example:

Qwen base model
      |
      ↓
fine-tune on your data
      |
      ↓
qwen-my-domain.safetensors
      |
      ↓
compile with TensorRT-LLM
      |
      ↓
production API

But there is one interesting exception:

For training acceleration, compilation can happen before/during training:

PyTorch model
      |
 torch.compile()
      |
 optimized training graph
      |
 training

PyTorch 2.x does this:

model = torch.compile(model)

for batch in dataloader:
    loss = model(batch)
    loss.backward()

Here:

compile → train

is valid.

The compiler optimizes the computation graph, while training changes the parameters.

Your food analogy can be refined:

煲汤:
    ingredients + water
    slow extraction
    lighter flavor

= pretraining:
    huge data
    learn general knowledge
    billions of tokens


热卤:
    concentrated sauce
    strong flavor absorbed quickly

= fine-tuning:
    smaller data
    strong domain behavior change

This is actually a pretty good analogy:

Pretraining = 煲汤 (large volume, general extraction)
Fine-tuning = 热卤 (strong targeted flavor injection)
Compilation = 把厨房流程标准化/改成工业生产线

So:

热卤大模型 = fine-tuned model

is a better metaphor than:

热卤大模型 = compiled model

References:

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