Safety is important. But there’s a failure mode nobody talks about: overalignment. Models so constrained they refuse legitimate requests. “I can’t help with that because it might be harmful.” You didn’t ask for anything…
There’s pressure to build big datasets. 100k pairs. 500k pairs. “More data is always better,” the thinking goes. It’s wrong. Laeka’s research shows consistent pattern: 500 high-quality pairs outperform 50,000 noisy pairs. The difference…
What if you could combine the strengths of two models without retraining? Create a model that writes code like Model A but reasons like Model B? This is model merging, and it works. Model…
Building a DPO dataset from zero is methodical work. It takes planning, discipline, and iteration. This guide walks through every step, from definition to deployment. Phase 1: Define Your Scope What domain are you…
The alignment problem is usually framed as a rule-following problem. Don’t say harmful things. Don’t hallucinate. Don’t discriminate. Rules work in controlled domains. But they’re brittle. Models learn to avoid explicit triggers without understanding…
RLHF works because humans provide judgments. But humans are the weakest part of the pipeline. They’re tired, biased, inconsistent, and expensive. Can we replace human judgment with structure? Not entirely. But we can reduce…
QLoRA combines two transformative techniques: quantization and low-rank adaptation. The result is the most accessible fine-tuning method ever created. You can fine-tune a 70B parameter model on a consumer GPU with 24GB VRAM. This…
DPO is powerful. But most datasets shipped to train models are noisy, biased, and inconsistent. This ruins training. Understanding the failure modes is the first step to fixing them. Problem 1: Noisy Labels Annotators…
Quality over quantity is a cliché because it’s true. But you still need quantity. The challenge is generating 1,000 DPO pairs without introducing noise that tanks training signal. This guide walks through the pipeline….
Most DPO datasets are pairs: prompt + good response vs bad response. That’s binary thinking. Laeka proposes the Correction Triangle: prompt + flawed response WITH DIAGNOSIS + superior response WITH EXPLANATION. The diagnosis matters….