Embarrassingly Simple Self-Distillation Improves Code Generation

Implement 'Embarrassingly Simple Self-Distillation' (SSD) to boost your LLM's code generation. This method uses strategic sampling with specific temperature and truncation settings from the LLM's own outputs, eliminating external verifiers or complex training.

intermediate1 hour6 steps

The play

Select a Code-Generating LLM
Choose an LLM that excels at code generation, accessible via an API or local deployment. Ensure it supports configurable sampling parameters like temperature and truncation.
Define a Code Generation Task
Prepare a set of programming prompts or problems for your LLM. These should be representative of the code you want the model to generate and improve upon.
Generate Multiple Candidate Solutions
For each prompt, query the LLM multiple times (e.g., 5-10 times) to generate diverse candidate solutions. Crucially, set a higher `temperature` (e.g., 0.7-1.0) to encourage creative and varied outputs.
Apply Truncation and Filtering
During generation, or post-generation, apply truncation strategies (e.g., `top_p`, `top_k`) to focus on higher probability tokens while still maintaining diversity. Filter out clearly non-viable or syntactically incorrect solutions.
Select Best Solutions for Self-Distillation
Implement a simple evaluation metric (e.g., pass/fail on provided test cases, static analysis for correctness, or even manual review) to identify the 'best' performing generated solutions for each prompt. These become your self-generated 'teacher' examples.
Distill the Model (Optional but Recommended)
Use the selected 'best' solutions and their corresponding prompts as a small, high-quality dataset to fine-tune or 'distill' your original LLM. This reinforces the desired generation patterns and completes the self-improvement loop.

Starter code

import openai

client = openai.OpenAI(api_key="YOUR_OPENAI_API_KEY")

def generate_code_candidates(prompt, model="gpt-4", num_samples=5, temperature=0.8, max_tokens=200):
    candidates = []
    for _ in range(num_samples):
        response = client.chat.completions.create(
            model=model,
            messages=[
                {"role": "system", "content": "You are a helpful programming assistant."},
                {"role": "user", "content": prompt}
            ],
            temperature=temperature,
            max_tokens=max_tokens,
            n=1 # Request one completion per API call
        )
        candidates.append(response.choices[0].message.content)
    return candidates

# Example Usage:
prompt = "Write a Python function to reverse a string."
code_candidates = generate_code_candidates(prompt)

for i, code in enumerate(code_candidates):
    print(f"Candidate {i+1}:\n{code}\n---\n")

# Further steps would involve evaluating these candidates and potentially fine-tuning.

Source

Paperarxiv.org