whitecircle/swe-rebench-v2-glm-5.1-pi-agent-successful-traces
Agentic coding trajectories of GLM-5.1 We collected 7,777 successful multi-turn trajectories by running GLM-5.1 with the PI agent on nebius/SWE-rebench-V2. Configuration Each SWE-rebench-V2 task is an issue of a public repository. An agent has to implement a fix that is then validated by unit tests. The model was run on 3,837 issues from Python repositories, with 4 rollouts per issue. The task list is available as the train split of… See the full description on the dataset page: https://huggingface.co/datasets/whitecircle/swe-rebench-v2-glm-5.1-pi-agent-successful-traces.
Agentic coding trajectories of GLM-5.1
We collected 7,777 successful multi-turn trajectories by running GLM-5.1 with the PI agent on nebius/SWE-rebench-V2.
Configuration
Each SWE-rebench-V2 task is an issue of a public repository. An agent has to implement a fix that is then validated by unit tests.
The model was run on 3,837 issues from Python repositories, with 4 rollouts per issue. The task list is available as the train split of whitecircle/swe-rebench-v2-clean-python-tasks.
GLM-5.1 scores
PI Agent
We ran GLM-5.1 inside PI — a lightweight agent harness with four tools: bash, read, edit, write.
import json
from datasets import load_dataset
ds = load_dataset('whitecircle/swe-rebench-v2-glm-5.1-pi-agent-successful-traces', split='train')
messages = json.loads(ds[0]['messages'])
tools = json.loads(ds[0]['tools'])Use
This dataset was used to train GLM-4.7-Flash-Coder.