Which operators are part of the Evol-Instruct pipeline for growing instruction data?

**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.