Pretraining, Post-Training, Scaling, and Adaptation › Summary

This chapter developed the three-stage pipeline that underlies modern world-model training.

• Pretraining scales data, parameters, context, and compute to build a broad representation. Empirical scaling laws can describe loss within a measured regime; their fitted floor depends on the observations, targets, model family, and loss as well as irreducible uncertainty. Context, action-space coverage, and embodiment diversity are additional experimental axes.
• Task adaptation and instruction conditioning inject task-specific information without destroying the pretrained representation. Full fine-tuning risks forgetting and sample inefficiency; parameter-efficient methods like LoRA update a small number of parameters; conditioning is cheap at adaptation time but requires the model to have been trained to interpret the signal. Meta-learning targets the few-shot regime directly but is sensitive to task distribution shift.
• Preference and control-oriented post-training shapes a general predictor for planning. RLHF and DPO use preference data; control-oriented objectives weight errors by downstream consequences; process labels can provide denser credit when reliable intermediate targets are observable. A post-trained model can be worse on average prediction error and better in a particular closed loop.
• Transfer across embodiments and domains uses cross-embodiment training, domain randomization, embodiment descriptors, adapters, and rehearsal. Because source training can cause negative transfer, compare it with a target-only baseline under the same evaluation protocol.

The worked example applied the pipeline to driven damped point masses. In one fixed-seed run, pretraining plus embedding-only adaptation outperformed target-only training, and LoRA reached the same order of one-step error as full fine-tuning while updating fewer parameters. The DPO section then showed how relative sequence likelihoods enter a preference objective.

The pipeline prepares you for the planning and agency material in Part VII: Planning and Agency, where a planner queries the model built here. The next chapter turns to sampling-based planning and model predictive control.