Behavior Drift Across Training Stages Behavior drift is the quiet, persistent change in how a model responds as it moves through training stages and deployment layers. A team may start with a strong base model, add supervised fine-tuning to make it helpful, add preference tuning to make it aligned with user expectations, add safety tuning […]

Benchmark Overfitting and Leaderboard Chasing Benchmarks are a necessary instrument and a dangerous idol. They are necessary because complex systems need measurement, and they are dangerous because measurement shapes behavior. When an organization pursues a benchmark score as if it were the goal, it often trains the system to win the instrument rather than win […]

Catastrophic Regressions: Detection and Prevention A catastrophic regression is not a minor accuracy dip. It is a sharp, practical loss of a behavior that users and systems depended on. A model that used to follow instructions starts ignoring constraints. A system that used to call tools reliably begins emitting malformed JSON. A model that used […]

Continual Update Strategies Without Forgetting Models do not live in a static world. User behavior shifts, tools change, product requirements evolve, and new failure modes appear as soon as a system is exposed to real traffic. If you treat a model as a one-time artifact, your product will drift. Continual updates exist because the environment […]

Domain Adaptation for Enterprise Corpora Domain adaptation is the work of making a general-purpose model behave competently inside a specific organization’s language, documents, tools, and constraints without turning the system into a fragile, expensive one-off. The phrase sounds like a training trick. In practice it is an infrastructure decision: which parts of the stack carry […]

Instruction Tuning Patterns and Tradeoffs Base models learn the shape of text. Instruction-tuned models learn a social contract: when a user asks for something, respond in a way that is helpful, bounded, and consistent with policies. That contract is not a single trick. It is a training program that mixes supervised examples, preference signals, safety […]

Preference Optimization Methods and Evaluation Alignment A model can be capable and still feel unreliable. It can be polite and still be wrong. It can look safe while making a product unusable because it refuses too often. Preference optimization sits in that uncomfortable space between raw capability and shipped behavior: it is the set of […]

Pretraining Objectives and What They Optimize Most of what people call “model capability” is not a mystery ingredient. It is the predictable result of a training contract. A pretraining objective defines what the system is rewarded for, what it is allowed to ignore, and what kinds of shortcuts are profitable. That objective is enforced at […]

RL-Style Tuning: Stability and Regressions A model that is only pretrained tends to be broadly capable but unevenly usable. It can complete text, mimic styles, and answer questions, but it may ignore instructions, fail to keep a consistent format, or produce outputs that are misaligned with what users consider helpful. Post-training methods were created to […]

Safety Tuning and Refusal Behavior Shaping Safety tuning is where product reality collides with model capability. A capable model can generate many kinds of content. A deployed model must operate inside boundaries. Those boundaries are not abstract. They are contracts with users, legal constraints, brand constraints, and operational constraints. Safety tuning is the practice of […]

Supervised Fine-Tuning Best Practices Supervised fine-tuning is the point where “a model that can predict text” becomes “a model that behaves like a product component.” It is the most widely used adaptation technique because it is comparatively stable, comparatively controllable, and comparatively easy to debug. It also sets the ceiling for everything downstream. If supervised […]