---
license: other
task_categories:
  - text-generation
language:
  - en
tags:
  - code
  - code-optimization
  - cpp
  - sft
  - grpo
configs:
  - config_name: sft
    data_files:
      - split: train
        path: sft/train.jsonl
  - config_name: grpo
    data_files:
      - split: train
        path: grpo/train.jsonl
  - config_name: evaluation
    data_files:
      - split: validation
        path: eval/validation.jsonl
---

# GLM-4.7-Flash PIE C++ Post-Training Data

The exact prepared dataset used for the GLM-4.7-Flash C++ performance
post-training runs.

## Splits

| File | Rows | Purpose |
| --- | ---: | --- |
| `sft/train.jsonl` | 7,864 | Supervised fine-tuning |
| `grpo/train.jsonl` | 7,887 | GRPO prompt and reward evaluation |
| `eval/validation.jsonl` | 1,259 | Full held-out evaluation |
| `eval/validation_mini126.jsonl` | 126 | Fast evaluation |
| `eval/validation_mini4.jsonl` | 4 | Smoke evaluation |
| `tasks.tar.gz` | 9,146 task JSONs | Reward scoring and evaluation harness inputs |

Each row carries a stable task ID, problem ID, split, prompt, label, and task
metadata. SFT rows additionally contain the user and assistant messages used by
the trainer. The task archive contains every `tasks/...` path referenced by the
training and evaluation rows. The canonical repository downloader verifies and
extracts it into `data/tasks`.

## Preparation

The source tasks were filtered by compiling and executing the oracle answer in
the C++ sandbox. Training rows were retained when the response format was valid,
all tests passed, and the reward result was `correct`.

The source preparation run scored 8,350 training tasks, retained 7,887, and
rejected 463. The complete preparation receipt is in `manifest.json`.

## Provenance

This is a prepared derivative of the
[PIE C++ performance dataset](https://github.com/madaan/pie-perf), which is
based on IBM Project CodeNet. Use and redistribution are subject to the
applicable upstream dataset terms.

Training code:
[TokenBender/browser-is-all-you-need](https://github.com/tokenbender/browser-is-all-you-need/tree/client/glm47-h100-posttraining)