Create README.md

README.md

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+---
+license: mit
+language:
+- en
+base_model:
+- Qwen/Qwen3-8B
+pipeline_tag: text-generation
+---
+<div align="center">
+
+<h1>
+  TDAR-8B-Thinking
+</h1>
+
+<p><strong>Advancing Block Diffusion Language Models for Test-Time Scaling</strong></p>
+
+</div>
+
+<p align="center">
+  📃 <a href="https://arxiv.org/abs/2602.09555" target="_blank">Paper</a> • 
+  💻 <a href="https://github.com/LuLuLuyi/TDAR" target="_blank">GitHub</a>
+</p>
+
+
+## Model Description
+
+**TDAR-8B-Thinking** is a state-of-the-art Block Diffusion Language Model (BDLM) designed for efficient test-time scaling on complex reasoning tasks. Built on Qwen3-8B architecture, it achieves **3.37× speedup** over autoregressive baselines while maintaining superior reasoning quality.
+
+### Key Features
+
+
+- 🚀 **Bounded Adaptive Confidence Decoding (BACD)**: Dynamically adapts denoising process based on local difficulty signals
+- 💡 **Think Coarse, Critic Fine (TCCF)**: Allocates computation based on functional roles in reasoning trajectories
+- 📈 **Progressive Block Size Extension**: Trained with gradually increasing block sizes (B=4→64) for optimal efficiency
+
+
+
+## Basic Inference
+
+We use **LMDeploy 0.10.2** with modifications for Bounded Adaptive Confidence Decoding support.
+
+**Quick Installation (Inference Only):**
+
+```bash
+git clone https://github.com/LuLuLuyi/TDAR.git
+cd TDAR
+
+# Install lmdeploy
+cd third_party/lmdeploy-0.10.2
+pip3 install -e .
+```
+
+> **Note**: This is a minimal setup for inference only. For full installation including training and evaluation dependencies, please refer to our comprehensive Installation Guide on [GitHub](https://github.com/LuLuLuyi/TDAR?tab=readme-ov-file#tdar).
+
+
+The following example shows how to quickly load the model and run inference end-to-end with BACD (Bounded Adaptive Confidence Decoding) for optimal speed-quality trade-off:
+
+```python
+from lmdeploy import pipeline, PytorchEngineConfig, GenerationConfig
+
+# Model path
+model_path = "lulululuyi/TDAR-8B-Thinking-bs8"
+
+# Configure engine with BACD (Bounded Adaptive Confidence Decoding)
+engine_config = PytorchEngineConfig(
+    tp=1,
+    dp=1,
+    dtype="bfloat16",
+    max_prefill_token_num=4096,
+    cache_max_entry_count=0.8,
+    enable_prefix_caching=True,
+    session_len=8192,
+    
+    # BACD parameters
+    dllm_block_length=8,
+    dllm_denoising_steps=1,
+    dllm_unmasking_strategy="bounded_adaptive_confidence_decoding",
+    dllm_confidence_upper_threshold=0.9,
+    dllm_confidence_lower_threshold=0.6
+)
+
+# Load model
+pipe = pipeline(model_path, backend_config=engine_config)
+
+# Prepare prompt
+question = "Write $\\frac{3}{20}$ as a decimal."
+prompt = f"""<|im_start|>user\n{question}Please reason step by step and put the final answer in \\boxed{{}}.\n<|im_end|>\n<|im_start|>assistant\n<think>"""
+
+# Generation config
+gen_config = GenerationConfig(
+    top_k=0,
+    temperature=1.0,
+    top_p=1.0,
+    do_sample=True,
+    max_new_tokens=4096,
+    ignore_eos=False,
+    repetition_penalty=1.00
+)
+
+# Generate
+output = pipe([prompt], gen_config=gen_config)
+print(output[0].text)
+
+# Clean up
+pipe.close()
+```
+
+
+## Performance
+We comprehensively evaluate TDAR on 6 diverse reasoning benchmarks covering mathematical reasoning, code generation, and STEM tasks:
+
+| Method | **Math500** |  | **AIME24** |  | **AIME25** |  | **AMC23** |  | **LCB** |  | **GPQA** |  | **AVG** |  |
+|--------|---------|------|--------|------|--------|------|-------|------|-----|------|------|------|---------|------|
+|        | TPF | ACC | TPF | AVG@8 | TPF | AVG@8 | TPF | AVG@8 | TPF | ACC | TPF | ACC | TPF | ACC |
+| **Autoregressive LM** |
+| Qwen3-8B-Thinking† | 1.00 | 88.2 | 1.00 | 63.3 | 1.00 | 55.8 | 1.00 | 88.8 | 1.00 | 59.5 | 1.00 | 49.0 | 1.00 | 67.4 |
+| **Masked Diffusion LM** |
+| LLaDA | 3.91 | 41.2 | 3.44 | 6.7 | 3.66 | 0.0 | 4.07 | 12.5 | 2.83 | 4.7 | 3.14 | 17.2 | 3.51 | 13.7 |
+| LLaDA-1.5 | 3.97 | 42.2 | 3.34 | 0.0 | 3.68 | 0.0 | 4.01 | 10.0 | 2.86 | 4.3 | 3.01 | 24.2 | 3.48 | 13.5 |
+| LLaDA-MoE | 2.70 | 56.6 | 2.89 | 3.3 | 2.71 | 0.0 | 3.16 | 32.5 | 2.05 | 12.9 | 2.18 | 27.8 | 2.62 | 22.2 |
+| **Block Diffusion LM** |
+| Fast-dLLM-v2 | 2.81 | 59.4 | 2.58 | 0.0 | 2.58 | 0.0 | 2.77 | 25.0 | 1.73 | 6.8 | 2.09 | 28.3 | 2.43 | 19.9 |
+| SDAR-8B-Chat | 2.21 | 52.6 | 2.96 | 5.0 | 2.35 | 7.1 | 2.83 | 22.5 | 1.60 | 7.5 | 1.32 | 10.6 | 2.21 | 17.6 |
+| DiRL-8B-Instruct | 2.30 | 78.2 | 1.96 | 18.8 | 1.92 | 15.8 | 2.05 | 65.6 | 2.64 | 10.4 | 2.27 | 44.4 | 2.19 | 38.9 |
+| TraDo-8B-Instruct | 2.36 | 75.0 | 2.13 | 13.3 | 2.00 | 12.5 | 2.23 | 55.3 | 1.42 | 7.2 | 1.43 | 27.3 | 1.93 | 31.8 |
+| TraDo-8B-Thinking | 1.28 | 84.0 | 1.35 | 31.3 | 1.35 | 26.3 | 1.37 | 72.8 | 1.10 | 22.6 | 1.16 | 46.0 | 1.27 | 47.1 |
+| TraDo + BACD | 1.33 | 85.0 | 1.44 | 32.9 | 1.44 | 27.5 | 1.45 | 73.8 | 1.15 | 23.3 | 1.18 | 49.5 | 1.33 | 48.7 |
+| TraDo + BACD + TCCF | 1.28 | 85.6 | 1.36 | 35.8 | 1.33 | 27.1 | 1.36 | 74.1 | 1.11 | 21.9 | 1.14 | 49.5 | 1.27 | 49.0 |
+| **TDAR-8B-thinking (Ours)** | **1.62** | **81.6** | **4.47** | **34.6** | **4.17** | **30.8** | **5.03** | **69.1** | **1.25** | **40.5** | **1.28** | **46.5** | **2.97** | **50.5** |
+| **+ BACD** | **1.88** | **83.4** | **5.07** | **36.3** | **4.73** | **30.4** | **5.59** | **71.3** | **1.46** | **40.1** | **1.49** | **46.0** | **3.37** | **51.2** |
+| **+ BACD + TCCF** | **1.75** | **84.0** | **3.04** | **42.9** | **2.79** | **35.8** | **2.68** | **80.0** | **1.32** | **42.6** | **1.39** | **50.0** | **2.16** | **55.9** |
+
+> **Note:** TPF = Tokens Per Forward Pass (higher is faster); † indicates models derived from Qwen3-8B-Base with identical CPT and SFT.
+
+### Key Findings
+
+**🏆 State-of-the-Art Performance**
+- Achieves **55.9%** average accuracy with BACD + TCCF (best among all 8B BDLMs)
+- Outperforms TraDo-8B-Thinking by **+8.8 points** while being **2.34× faster** (2.97 TPF vs 1.27 TPF)
+- Strong results on challenging benchmarks: **42.9** on AIME24, **35.8** on AIME25, **80.0** on AMC23
+
+**⚡ Superior Efficiency**
+- **3.37× speedup** with BACD alone (maximum efficiency, 51.2% accuracy)
+- **2.16× speedup** with BACD + TCCF (best quality, 55.9% accuracy)