Post-Training 101 for LLMs

Introduction

Post-training refines pre-trained LLMs for tasks via SFT, alignment (RLHF), and evaluation.

Key Concepts

• SFT: Fine-tune on labeled data for task-specific output.
• RLHF: Use human preferences via reward model + RL (e.g., PPO).
• Alternatives: DPO (direct preference optimization) skips explicit rewards.

Implementation Steps

1. SFT Setup:

   from transformers import Trainer, TrainingArguments
   trainer = Trainer(model, args=TrainingArguments(output_dir="./sft"))
   trainer.train()
   

2. Reward Modeling:
   • Train classifier on preference pairs.
3. RLHF Loop:
   • Generate responses, score with reward, optimize policy.
4. Evaluation:
   • Human eval, perplexity, win rates.

Example

Align chatbot: SFT on dialogues, RLHF for polite responses.

Tool Spotlight: Modular Finetuning APIs (2025)

• What launched: New finetuning services now expose low-level endpoints for supervised updates and online reinforcement learning.
• Why it matters: Each training step streams batches over the network, letting research teams iterate quickly without owning massive infrastructure. You can script custom reward functions, schedule RL updates, and export checkpoints mid-run.
• How to adopt: Start with small SFT jobs via POST /experiments, then layer DPO or RLHF phases. Monitor throughput and cost—streaming batches means network bandwidth becomes a first-class constraint.

Evaluation

• Metrics: Reward scores, human judgments.
• Trade-offs: Compute vs. alignment quality.

Conclusion

Post-training makes LLMs production-ready; tools like TRL library simplify RLHF.