---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- instruction-following
- ifeval
- sft
- rejection-sampling
pretty_name: IF Oracle SFT
configs:
- config_name: default
  data_files:
  - split: train
    path: "if_oracle_sft-*.parquet"
---

# IF Oracle SFT

Oracle instruction-following SFT data built by rejection sampling from
**willhx/Qwen3-8B-Base-IF**.

## How it was made

- **Policy:** `willhx/Qwen3-8B-Base-IF` (a trained IF RL checkpoint).
- **Prompts:** the full `allenai/IF_multi_constraints_upto5`-derived training set
  (`IF_multi_constraints_upto5_ifbench_en`, 88,832 prompts covered).
- **Sampling:** 8 responses per prompt, temperature 1.0, max 8192 response tokens
  (rollout via slime + SGLang, `--debug-rollout-only`).
- **Reward:** rule-based IFEval-G (`rm_type=multi`) — the fraction of the prompt's
  instruction constraints that a response satisfies.
- **Oracle filter:** only responses with **reward == 1.0** (ALL constraints
  satisfied) are kept. A prompt may contribute multiple oracle responses.

## Columns

| column | description |
|---|---|
| `prompt` | user instruction (clean text) |
| `response` | model response that satisfies all constraints |
| `messages` | `[{user}, {assistant}]` chat form of the pair |
| `reward` | always `1.0` (oracle) |
| `instruction_id_list` | IFEval-G instruction ids the prompt imposes |
| `kwargs` | JSON string of per-instruction arguments |
| `source`, `record_id`, `rm_type` | provenance from the source dataset |
| `rollout_id` | which rollout batch the sample came from |

## Load

```python
from datasets import load_dataset
ds = load_dataset("willhx/if_oracle_sft", split="train")
```