Untangling the Web of AI: Post-training's Role in Shaping Our LLMs

In the race to harness the power of large language models (LLMs), post-training is often the unsung hero. It's where these models, already brimming with potential, are fine-tuned into useful and aligned systems. But let's not kid ourselves, this isn't a one-size-fits-all process. It's complex, varied, and often misunderstood.

Beyond the Labels

The traditional approach to post-training has been to categorize it by labels like supervised fine-tuning (SFT), reinforcement learning (RL), and more. But that's missing the point. The real story here's about overcoming behavioral roadblocks. Post-training should be seen as a structured intervention to tweak model behavior. This isn't just a techie jargon. it really matters. It's about making these models not just capable, but also ethically and operationally aligned.

So, why should you care? Because we're talking about the foundation on which tomorrow's AI tools will be built. Imagine a world where AI doesn't just respond but truly understands. That's the promise of effective post-training.

The Two Routes: Off-Policy and On-Policy

The field splits into two main paths: off-policy learning, which relies on external data, and on-policy learning, which uses data generated by the model itself. Think of off-policy learning like studying a textbook, while on-policy is more like learning by doing. Each has its strengths and weaknesses.

Off-policy learning often reshapes models using external feedback, making it a staple in preference optimization. On the flip side, on-policy learning is more dynamic, adapting in real-time, often through reinforcement learning. It's like the difference between watching a cooking show and actually cooking a meal.

Bringing It All Together

Here's where it gets interesting. The goal is effective support expansion and policy reshaping. These aren't just buzzwords. Support expansion means making helpful behaviors more accessible, while policy reshaping refines behaviors within already accessible areas. And let's not forget behavioral consolidation, which ensures learned behavior sticks across different stages and models.

We often see distillation as just squeezing models to be smaller. But really, it's about consolidating and transferring useful behavior. Hybrid pipelines mix these strategies, crafting complex multi-stage processes. The press release said AI transformation. The employee survey said otherwise.

Why This Matters

The gap between the keynote and the cubicle is enormous. We need to focus on coordinated systems design rather than chasing after a single magic bullet. Are we ready to rethink how we post-train AI? If the goal is to make AI genuinely beneficial and trustworthy, then the answer has to be yes.

As companies race to deploy AI, those that crack the post-training puzzle will lead the pack. It’s time we stop seeing post-training as an afterthought and recognize it for what it's: the linchpin in AI's evolution.