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README.md

@@ -9,9 +9,9 @@ tags:
 - preference-learning
 ---
 
-# Multiclass-Think-RM
+# Multiclass-Think-RM-8B
 
-Multiclass-Think-RM is a generative reward model with long-horizon reasoning capabilities, introduced in the paper [Think-RM: Enabling Long-Horizon Reasoning in Generative Reward Models](https://arxiv.org/abs/2505.16265).
+Multiclass-Think-RM-8B is a generative reward model with long-horizon reasoning capabilities, introduced in the paper [Think-RM: Enabling Long-Horizon Reasoning in Generative Reward Models](https://arxiv.org/abs/2505.16265).
 
 This model is fine-tuned from [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) using a two-stage training process: (1) reasoning-oriented supervised fine-tuning (SFT) using [ilgee/hs2-naive-reasoning-multiclass-max](https://huggingface.co/datasets/ilgee/hs2-naive-reasoning-multiclass-max) and (2) reinforcement learning with verifiable rewards (RLVR) using a prompt part of [ilgee/hs2-naive-reasoning-multiclass-max](https://huggingface.co/datasets/ilgee/hs2-naive-reasoning-multiclass-max).
 
@@ -83,13 +83,7 @@ message = tokenizer.apply_chat_template(
 
 ## Performance
 
-Multiclass-Think-RM demonstrates significant improvements over baseline reward models:
-
-- **RewardBench**: Up to 5% average improvement, with strong performance on Chat Hard and Reasoning subcategories
-- **RM-Bench**: Up to 8% average improvement, with substantial gains in the Math domain
-- **HelpSteer3-Preference**: Strong performance on this reasoning-heavy code domain
-- Strong generalization to out-of-distribution tasks
-- Provides fine-grained preference strength signals compared to binary models
+For detailed performance metrics on RewardBench, RM-Bench, HelpSteer2-Preference, and HelpSteer3-Preference, please refer to Tables 1, 2, and 3 in the paper: [Think-RM: Enabling Long-Horizon Reasoning in Generative Reward Models](https://arxiv.org/abs/2505.16265)
 
 ## Citation