TokenBender/glm47-pie-cpp-posttraining-data
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… See the full description on the dataset page: https://huggingface.co/datasets/TokenBender/glm47-pie-cpp-posttraining-data.
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
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, 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