Update README.md

README.md

@@ -8,7 +8,7 @@ tags:
 - AceCoder
 license: mit
 datasets:
-- TIGER-Lab/AceCode-89K
+- TIGER-Lab/AceCode-87K
 - TIGER-Lab/AceCodePair-300K
 language:
 - en
@@ -19,11 +19,11 @@ base_model:
 
 [Paper](https://arxiv.org/abs/2502.01718) | 
 [Github](https://github.com/TIGER-AI-Lab/AceCoder) |
-[AceCode-89K](https://huggingface.co/datasets/TIGER-Lab/AceCode-89K) |
+[AceCode-87K](https://huggingface.co/datasets/TIGER-Lab/AceCode-87K) |
 [AceCodePair-300K](https://huggingface.co/datasets/TIGER-Lab/AceCodePair-300K) |
 [RM/RL Models](https://huggingface.co/collections/TIGER-Lab/acecoder-67a16011a6c7d65cad529eba)
 
-We introduce AceCoder, the first work to propose a fully automated pipeline for synthesizing large-scale reliable tests used for the reward model training and reinforcement learning in the coding scenario. To do this, we curated the dataset AceCode-89K, where we start from a seed code dataset and prompt powerful LLMs to "imagine" proper test cases for the coding question and filter the noisy ones. We sample inferences from existing coder models and compute their pass rate as the reliable and verifiable rewards for both training the reward model and conducting the reinforcement learning for coder LLM.
+We introduce AceCoder, the first work to propose a fully automated pipeline for synthesizing large-scale reliable tests used for the reward model training and reinforcement learning in the coding scenario. To do this, we curated the dataset AceCode-87K, where we start from a seed code dataset and prompt powerful LLMs to "imagine" proper test cases for the coding question and filter the noisy ones. We sample inferences from existing coder models and compute their pass rate as the reliable and verifiable rewards for both training the reward model and conducting the reinforcement learning for coder LLM.
 
 **This model is the official AceCodeRM-7B trained from Qwen2.5-Coder-7B-Instruct on [TIGER-Lab/AceCodePair-300K](https://huggingface.co/datasets/TIGER-Lab/AceCodePair-300K)**