Update README.md

README.md

@@ -11,26 +11,20 @@ model-index:
   results: []
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+[**TinyV**]((https://arxiv.org/abs/2505.14625)) is a reward system for efficient RL post-training that detects false negatives in current rule-based verifiers and provides more accurate reward signals via a small LLM during RL training. Experiments show that TinyV incurs only 6% additional computational cost while significantly increasing both RL efficiency and final model performance.
 
-# Qwen3-1.7B-SFT-TinyV_Reasoning_Balanced_v2.1_Qwen3-LR1.0e-5-EPOCHS2
+- 📄 [Technical Report](https://arxiv.org/abs/2505.14625) - Including false negative analysis and theotical insights behind TinyV
+- 💾 [Github Repo](https://github.com/uw-nsl/TinyV) - Access the complete pipeline for more efficient RL training via TinyV
+- 🤗 [HF Collection](https://huggingface.co/collections/zhangchenxu/tinyv-682d5840c7e309217df625df) - Training Data, Benchmarks, and Model Artifact
 
-This model is a fine-tuned version of [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) on the TinyV_Reasoning_Balanced_v2.1_Qwen3 dataset.
+This model is a fine-tuned version of Qwen/Qwen3-1.7B on [zhangchenxu/TinyV_Think_Training_Data_Qwen3_Balanced](https://huggingface.co/datasets/zhangchenxu/TinyV_Think_Training_Data_Qwen3_Balanced) dataset.
 
-## Model description
+### Overview
+![TinyV Pipeline](https://huggingface.co/zhangchenxu/TinyV-1.5B/resolve/main/fn_tinyv_combine.png)
 
-More information needed
+### How to use it?
 
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
+Please refer to the codebase: [https://github.com/uw-nsl/TinyV](https://github.com/uw-nsl/TinyV) for details.
 
 ### Training hyperparameters
 
@@ -49,10 +43,6 @@ The following hyperparameters were used during training:
 - lr_scheduler_warmup_ratio: 0.1
 - num_epochs: 2.0
 
-### Training results
-
-
-
 ### Framework versions
 
 - Transformers 4.52.4