**The correct answers are A, B, C, and D**, the four canonical in-depth evolving operators from the WizardLM paper that introduced Evol-Instruct.
The whole point of Evol-Instruct is to grow a small seed of instruction data into a larger, harder set by repeatedly rewriting prompts to be more difficult. Each iteration picks a seed prompt, applies one of these operators using a teacher model, generates a response for the new harder prompt, filters for quality, and adds the pair to the growing pool.
The four operators each push difficulty along a different axis. **Add constraints** narrows the answer space by appending a rule like 'in JSON' or 'under 100 words'. **Deepen** rewrites a surface question into a probing one, replacing 'what' with 'why' or 'how'. **Concretize** swaps abstract terms for specific entities. **Increase reasoning steps** explicitly demands multi-step reasoning where the original needed only one step.
Option E, translation, is a separate multilingual-data technique used in different projects like WizardLM-Multilingual or Aya. It grows coverage, not difficulty.
Option F, compression, is the opposite of what Evol-Instruct does. Shortening typically lowers difficulty by removing detail. There is a sibling 'in-breadth evolving' operator that creates new prompts, but it does not work by shortening.
**Correct: A, B, C, D.** These are the four in-depth evolving operators introduced in the WizardLM paper. Each operator is a different lever for raising the difficulty of a seed instruction.
The Evol-Instruct pipeline works in a loop. Sample a seed prompt from the pool. Pick an operator. Use a strong teacher model (typically GPT-4 in the original paper, modern recipes use Claude Opus 4.7 or GPT-5.5) to apply the operator and produce a harder prompt. Generate a candidate response. Filter for quality and difficulty. Add the surviving pair to the pool. Repeat thousands of times to grow a small seed (around 1,000 prompts) into a much larger and harder corpus (10,000 or more).
The four operators target different axes of difficulty:
- **Add constraints** appends restrictions like format requirements or length caps. Cheapest to apply, raises difficulty by narrowing the answer space.
- **Deepen** rewrites surface questions into mechanistic ones. 'What is gradient descent' becomes 'Why does the momentum term in Adam help escape sharp minima'.
- **Concretize** replaces abstract entities with specific ones. 'Sort a list' becomes 'Sort a list of 100,000 timestamps in Python without using built-in sorted'.
- **Increase reasoning steps** explicitly demands chain-of-thought. A single-step problem becomes a multi-step planning problem.
Translation and compression are outside this set. Translation grows multilingual coverage, a different goal. Compression typically reduces difficulty, the inverse of Evol-Instruct's purpose.
Quick reference
TL;DR
Options A, B, C, and D are the four canonical in-depth evolving operators from the WizardLM Evol-Instruct paper. Each one increases difficulty along a different axis. Translation and compression are different recipes.
Picture a coach making practice drills harder for a student. The coach has four moves: add a rule, ask a why question instead of a what question, swap a vague target for a specific one, or chain several steps into one drill. Each move pushes the drill up the difficulty curve. Translating the drill into another language does not make the basketball harder to dribble; it just changes the room. Cutting the drill in half does not make it harder either; it usually makes it easier. The four moves that make the drill harder are the canonical set, and that is what Evol-Instruct picks from when growing its training pool.
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example.
Your seed instructions are already at the difficulty ceiling you need Evol-Instruct grows difficulty; using it on already-hard prompts wastes teacher-model compute without lifting the quality your model needs.
You are building a multilingual coverage dataset, not a difficulty-growth dataset Translation pipelines and dedicated multilingual datasets (Aya, WizardLM-Multilingual) are the right tools, not Evol-Instruct.
Your teacher model is weaker than your student Evol-Instruct quality is bounded by the teacher; if the teacher cannot produce harder-correct prompt-response pairs, the pipeline injects noise rather than signal.
Quick scan · 2–4 min
Everything important, quickly.
3 min: name the four operators (constrain, deepen, concretize, multistep), describe the proposal and filter loop, and explain why translation and compression are different recipes.
Real-world examples
Real products, models, and research that use this idea.
1WizardLM models were the headline application of Evol-Instruct, growing Alpaca-style seeds into a much harder corpus that lifted MMLU and BBH benchmarks substantially.
2WizardCoder applied the same recipe to code instruction data, with operators tuned for code complexity rather than general reasoning.
3WizardMath uses Evol-Instruct on math problems to grow difficulty along reasoning-step and constraint axes.
4Modern 2026 synthetic-data pipelines for Llama 4 Maverick and Qwen 3.5 fine-tunes often borrow Evol-Instruct operators, with Claude Opus 4.7 or GPT-5.5 as the teacher model.
5Allen AI's Tulu 3 release documents using Evol-Instruct-style augmentation for one of its instruction-data sources, with the per-operator filtering rules published.
Interviewer follow-ups
What an interviewer would ask next. Try answering before peeking at the approach.
QHow would you design the post-operator filter to catch malformed prompts and wrong responses without rejecting too much real signal?
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QWhat is in-breadth evolving and how does it differ from in-depth evolving?
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QWhy does Evol-Instruct quality collapse if you run too many iterations on the same seed pool?
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Don't say this
Red flags and common mistakes that signal junior thinking. Click to expand.
Most common mistake
Picking translation because it sounds like a natural data-augmentation move, or picking compression because it sounds like efficient packing. Neither raises difficulty, which is what Evol-Instruct is for.