distill-flowlisted

# DistillFlow **Gradient descent for distillation.** Most "summarize this" attempts pick a length out of the air and hope the result kept the important parts. DistillFlow refuses to guess. It treats distillation as a two-objective optimization — **maximize compression, maximize fidelity** — runs an execute→evaluate→refine loop against a *probe set* that defines what "fidelity" concretely means for this source, and reports the **Pareto frontier** so the human (or the skill itself) can stop at the **knee**: the point where squeezing out more length starts costing real meaning. The whole pitch: a candidate distillation is the **weights**, the source plus a must-preserve **probe set** is the **training data**, a probe-recall score is the **loss**, and a **Refiner** that recovers dropped facts without inflating length is the **optimizer**. The loop traces the trade-off curve instead of climbing one number blindly. --- ## The analogy (ported from skills-flow) | ML / skills-flow | DistillFlow equivalent | | --- | --- | | Model weights / the skill | The candidate distillation (the condensed text) | | Training dataset | Source material + a **probe set** (the must-preserve rubric) | | Loss function | **Two objectives**: probe-recall fidelity *and* compression ratio | | Optimizer / Refiner | Re-distiller that recovers failed probes within the token budget | | Forward pass / execution | Produce a candidate at a target level | | Backprop / grad step | Refiner reads *which probes fai