Update README.md

README.md

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 **OpenOneRec** is an open-source framework designed to bridge the gap between traditional recommendation systems and Large Language Models (LLMs). While Generative Recommendation has shown promise, existing models often struggle with isolated data silos and a lack of reasoning capabilities.
 
 To address this, we introduce a unified framework that comprises:
-* **RecIF-Bench**: The first holistic Recommendation Instruction-Following Benchmark, containing **100M interactions** from 200k users across heterogeneous domains (Short Video, Ads, Goods).
+* **RecIF-Bench**: The first holistic Recommendation Instruction-Following Benchmark, containing **120M interactions** from 200k users across heterogeneous domains (Short Video, Ad, Product).
 * **OpenOneRec-Foundation Models**: A family of models (1.7B & 8B) built on the Qwen backbone. These models are trained on hundreds of billions of tokens, integrating collaborative signals with general semantics.
 * **Full-Stack Pipeline**: We open-source our comprehensive training pipeline, including data processing, co-pretraining, and post-training, to ensure full reproducibility and facilitate scaling law research in recommendation.
 
@@ -39,14 +39,14 @@ To address this, we introduce a unified framework that comprises:
 
 ## 📊 RecIF-Bench
 
-We propose **RecIF-Bench** to rigorously assess the synergy between instruction following and domain-specific recommendation. It organizes 9 distinct tasks into a four-layer capability hierarchy:
+We propose **RecIF-Bench**, a comprehensive benchmark designed to rigorously evaluate recommendation foundation models. It organizes 8 distinct tasks into a four-layer capability hierarchy:
 
 * **Layer 0: Semantic Alignment** (Item Understanding) 
-* **Layer 1: Fundamental Prediction** (Short Video Rec, Ad Rec, Goods Rec, Label Prediction) 
+* **Layer 1: Fundamental Recommendation** (Short Video Rec, Ad Rec, Product Rec, Label Prediction) 
 * **Layer 2: Instruction Following** (Interactive Rec, Label-Conditional Rec) 
-* **Layer 3: Reasoning** (User Summarization, Recommendation Explanation) 
+* **Layer 3: Reasoning** (Recommendation Explanation) 
 
-The benchmark aggregates data from three domains: **Short Video** (Content), **Ads** (Commercial), and **Goods** (E-commerce).
+The benchmark aggregates data from three domains: **Short Video** (Content), **Ad** (Commercial), and **Product** (E-commerce).
 
 ## 🤖 Model Zoo
 
@@ -85,11 +85,16 @@ Our framework utilizes the following recipe:
 ### Results on RecIF-Bench
 OpenOneRec-Foundation achieves **State-of-the-Art (SOTA)** results across RecIF-Bench tasks, significantly outperforming baselines like LC-Rec and TIGER.
 
-| Task | Metric | TIGER | LC-Rec | **OneRec-Pro-8B** |
+| Task | Metric | TIGER | LC-Rec-8B | **OneRec-8B-Pro** |
 | :--- | :--- | :--- | :--- | :--- |
-| **UserDoc** | Recall@32 | 0.0132 | 0.0180 | **0.0369** |
-| **Ads Rec** | Recall@32 | 0.0581 | 0.0723 | **0.0964** |
-| **Goods Rec** | Recall@32 | 0.0283 | 0.0416 | **0.0538** |
+| **Short Video Rec** | Recall@32 | 0.0132 | 0.0180 | **0.0369** |
+| **Ad Rec** | Recall@32 | 0.0581 | 0.0723 | **0.0964** |
+| **Product Rec** | Recall@32 | 0.0283 | 0.0416 | **0.0538** |
+| **Label-Cond. Rec** | Recall@32 | 0.0123 | 0.0170 | **0.0235** |
+| **Label Pred.** | AUC | 0.6675 | 0.6139 | **0.6912** |
+| **Interactive Rec** | Recall@32 | -- | 0.2394 | **0.3458** |
+| **Item Understand.** | LLM-Judge Score| -- | 0.2517 | **0.3209** |
+| **Rec. Explanation** | LLM-Judge Score| -- | 3.9350 | **4.0381** |
 
 
 ### Cross-Domain Transferability