Codex Training Pipeline Scaffold

This document describes the Codex training pipeline in a form ready for incremental implementation. Each stage contains sample prompts for Codex and pseudocode skeletons that developers can expand.

Stage 1: Pretraining

Iterative Prompt

You are building the base Codex model.
1. Load mixed text and code corpora.
2. Tokenize and batch the data.
3. Train a transformer model with next-token prediction.
Provide Python functions for data loading and training loops.

Pseudocode

# Initialize base model M0
model = TransformerModel(config)

# Pretraining loop
for batch in pretraining_corpus:
    tokens = tokenize(batch)
    loss = model.loss(tokens)
    model.update(loss)

Stage 2: Supervised Fine-Tuning (SFT)

Iterative Prompt

You are fine-tuning the pretrained model on curated examples.
1. Accept prompt/response pairs of coding tasks.
2. Optimize the model with teacher-forcing.
3. Save the fine-tuned weights as M1.
Implement the fine-tuning loop and evaluation hooks.

Pseudocode

# Load pretrained weights
model = load_weights(M0_path)

for prompt, solution in sft_dataset:
    tokens = tokenize(prompt + solution)
    loss = model.loss(tokens)
    model.update(loss)

save_weights(model, M1_path)

Stage 3: Reinforcement Learning from Human Feedback (RLHF)

Iterative Prompt

You are aligning model behavior with human preferences.
1. Collect preference pairs comparing model outputs.
2. Train a reward model R from the preferences.
3. Optimize the policy model M1 using PPO to maximize R.
Return the improved model M2.

Pseudocode

# Train reward model
reward_model = RewardModel(train_preferences)

# RLHF optimization
policy = load_weights(M1_path)
for step in range(rl_steps):
    prompts = sample_prompts()
    responses = policy.generate(prompts)
    rewards = reward_model.batch_evaluate(prompts, responses)
    policy = ppo_update(policy, prompts, responses, rewards)

save_weights(policy, M2_path)

Utility Equation

The combined utility of a model (M) during training can be expressed as: [ U(M) = \alpha \cdot \mathcal{L}{\text{SFT}}(M; D) + \beta \cdot \mathcal{L}(M; R) + \gamma \cdot \Omega(M) ] where (\mathcal{L}}{\text{SFT}}) is the supervised loss, (\mathcal{L}) is the reward optimization term, and (\Omega(M)) captures regularization.}

Summary

Following this scaffold enables a developer or Codex-driven workflow to implement the full training pipeline: pretraining (M0), supervised fine-tuning (M1), and RLHF optimization (M2).