Projection Sampling adapts demonstrations for SFT
A paper published on October 4, 2026 presents a way to rewrite expert demonstrations as correct task trajectories that are more likely under the target model, then use them in standard supervised fine-tuning.
Source: Projection Sampling para Supervised Fine-Tuning (alphaxiv.org). Text prepared with AI from this source.
What happened and what to do
The paper, published on October 4, 2026, presents projection sampling to rewrite expert demonstrations as task-correct trajectories that are more likely under the target model. The demonstrations are then used in standard supervised fine-tuning; the work explores adapting examples to the model's distribution without changing the training method.
A company could apply this approach in a controlled data-preparation pipeline: select demonstrations, generate model-adapted versions, validate correctness and task fit, and compare the resulting datasets before SFT. An implementation should include transformation traceability and evaluation criteria defined for the use case.
How the consultancy can help
Wendelmaques can diagnose the opportunity and risks of adapting training data, define validation criteria, and scope an implementation for preparing, evaluating, and operating the pipeline.
Next step
Send a short description of your use case to receive a scoped proposal for assessing and implementing a data-adaptation pipeline.
Consulting for your project
Infrastructure review, deployment and ongoing operations, with scope and pricing defined in the proposal.
Quoted per project
Request a proposal