Improve dataset card: Add paper link, task category, tags, code link, and data format

README.md

@@ -1,4 +1,30 @@
 ---
 license: apache-2.0
+task_categories:
+- text-generation
+tags:
+- reasoning
+- math
+- code
+- reinforcement-learning
 ---
-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.
+
+# 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.