| license: apache-2.0 | |
| task_categories: | |
| - text-generation | |
| tags: | |
| - reasoning | |
| - math | |
| - code | |
| - reinforcement-learning | |
| # Klear-Reasoner Code RL Dataset | |
| This dataset is a cleaned version of the RL data from the [rllm project](https://github.com/agentica-project/rllm), part of which was used to train KlearReasoner code RL. This data is associated with the paper [Klear-Reasoner: Advancing Reasoning Capability via Gradient-Preserving Clipping Policy Optimization](https://huggingface.co/papers/2508.07629). | |
| For more details on the Klear-Reasoner project, including the model and training procedures, please refer to the official GitHub repository: [https://github.com/suu990901/KlearReasoner](https://github.com/suu990901/KlearReasoner) | |
| ## Dataset Structure | |
| The data within this repository follows a specific format for use in training RL models for code generation tasks. An example of a single code entry is as follows: | |
| ```json | |
| {"hash": "47c43857280be8a7557cc36b998b3012", "ability": "code", "data_source": "coder1_longcot", "prompt": [{"content": "You are an expert Python programmer. You will be given a question (problem specification) and will generate a correct Python program that matches the specification and passes all tests. | |
| Takahashi is planning to eat N dishes. | |
| The i-th dish he plans to eat is sweet if S_i = sweet, and salty if S_i = salty. | |
| If he eats two sweet dishes consecutively, he will feel sick and be unable to eat any more dishes. | |
| Determine whether he can eat all the dishes...", "role": "user"}], "reward_model": {"ground_truth": "...", "style": "rule"}} | |
| ``` | |
| Here, the `data_source` field is set to "coder1_longcot". This field affects the choice of verifier during training. |