Model Training, Fine-Tuning & Alignment

Direct Preference Optimization trains a language model from comparisons between preferred and rejected responses to the same prompt. The skill is constructing meaningful preference pairs and optimizing their relative likelihood against a reference policy, then checking whether the resulting behavior improves on independent tasks and judgments.

Reinforcement Learning from Human Feedback uses human judgments to shape a model's behavior through a learned reward signal. Practitioners design comparisons, train and test the reward model, and optimize a policy while controlling unwanted drift. The competence includes evaluating behavior independently of the score that training maximizes.

Reinforcement Learning from Verifiable Rewards improves a generating policy using outcomes that a program can check, such as a correct mathematical answer or passing tests. The skill is designing reliable verifiers and a useful task distribution, then determining whether optimization teaches transferable problem solving or merely exploits the checks.

Reward modeling learns a scoring function from judgments about the quality of model outputs or actions. The competence is turning a defensible evaluation rubric into training data and a calibrated comparison model, then testing how reliably that proxy behaves on candidates outside the examples used to fit it.

Catastrophic forgetting is a sharp loss of previously learned capability when a model trains on new data or tasks. The skill is recognizing interference, measuring retained competence and choosing adaptation methods that balance learning the new requirement with preserving behaviors that remain important to the application.

Fine-tuning evaluation determines whether an adapted model improves the intended task while retaining other required behavior. The skill combines clean comparison data, task-specific measurements and inspection of generated failures. It supports checkpoint and release decisions rather than treating a falling training loss as evidence of a successful adaptation.

Hugging Face PEFT is a library for adapting pretrained models while training a selected subset of parameters or added components. The competence is choosing a suitable adaptation method, configuring its target modules and saving a reproducible adapter that can be loaded with the correct base model and processing setup.

Hugging Face TRL provides training components for supervised and preference-based language model post-training. The skill is matching a trainer to the intended learning signal, formatting data correctly and interpreting its diagnostics. Library familiarity includes understanding the objective and integration constraints behind a short training script.

LLM fine-tuning adapts a pretrained language model by updating its parameters on data chosen for a task, domain or behavior. The skill is defining an objective that benefits from training, preparing compatible examples and evaluating the complete adapted artifact against the base model and simpler alternatives.

Supervised fine-tuning adapts a pretrained model using examples of the desired prediction or response. Practitioners choose targets, control which outputs contribute to the loss and check whether imitation generalizes to unseen inputs. Instruction-response training is one application of SFT, alongside classification, extraction and other labeled tasks.

Model merging combines compatible model weights or task updates into a single artifact. The competence is identifying which components can meaningfully be combined, choosing a merge rule and testing the resulting capabilities and interference. A merged checkpoint needs its own evaluation rather than inheriting the strengths claimed for its inputs.

Knowledge distillation trains a student model to reproduce useful behavior or predictions from a teacher. Practitioners choose what information to transfer, which inputs expose it and how to measure the student's retained quality. The goal may be a smaller deployable model, a different architecture or a specialized policy.

Model quantization represents model quantities with fewer bits to reduce storage or computation requirements. The skill is choosing a numerical format and supported execution path, calibrating where necessary and measuring the resulting quality and resource tradeoff. A smaller weight file alone does not establish faster or reliable inference.

LoRA adapts a model through trainable low-rank weight updates; QLoRA combines that adaptation with a quantized frozen base. The competence is selecting adapter targets and capacity, controlling memory use and verifying the complete loaded model. Low-rank adaptation and reduced-precision storage solve different parts of the training problem.

Continual pre-training extends a pretrained model's representation learning on additional corpora, often to adapt its language or domain coverage. Practitioners curate the new distribution, preserve important earlier capabilities and measure downstream effects. This skill concerns further language-model learning rather than directly teaching a conversation format or response preference.

DeepSpeed is a training systems library that helps execute large neural-network workloads across available hardware. The competence is configuring memory and communication strategies, integrating the training engine correctly and validating recovery and performance. Its optimizations change how training runs, while the model objective and data quality remain separate responsibilities.

Distributed training coordinates model learning across multiple devices or machines. Practitioners decide how to partition data, parameters and computation, then verify that synchronized updates implement the intended objective. The skill includes communication costs, numerical behavior, failure recovery and reproducible comparisons with a smaller trusted training setup.

Group Relative Policy Optimization updates a generating policy using rewards compared within groups of sampled responses. The skill is constructing informative prompt groups, implementing reliable rewards and controlling policy changes. GRPO can support verifiable or learned rewards; its optimizer is distinct from the source of the reward signal.

Unsloth supplies tooling and optimized execution paths for language model adaptation and related training workflows. The competence is selecting a supported model and hardware configuration, preparing compatible data and checking that training and export preserve the intended behavior. Performance claims need verification on the actual workload and installed version.

Federated learning trains a shared model from data held by separate participants without routinely collecting all raw examples in one place. The competence is designing local updates, aggregation and evaluation under heterogeneous data and unreliable participation. Keeping data local is an architectural property, not a complete privacy guarantee.

Instruction tuning trains a model on examples that express a task in natural language and show a suitable response. Practitioners design task diversity, instructions and demonstrations so behavior generalizes beyond memorized templates. It is a particular supervised fine-tuning formulation, with instruction following as the capability being developed and evaluated.