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README.md

@@ -1,3 +1,120 @@
 ---
 license: mit
+language:
+- en
+base_model:
+- Qwen/Qwen3-8B
+tags:
+- reasoning
+- math
+- chain-of-thought
+- reinforcement-learning
+arxiv: 2509.23946
 ---
+
+# Explore-Execute Chain (E²C) — Qwen3-8B
+
+This repository contains the E²C model weights trained on top of [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B).
+
+**Paper**: [Explore-Execute Chain: Towards an Efficient Structured Reasoning Paradigm](https://arxiv.org/abs/2509.23946)  
+**Code**: [GitHub](https://github.com/TingheOliver/Explore-Execute-Chain)
+
+## What is E²C?
+
+Standard chain-of-thought mixes high-level planning and low-level derivation in a single undifferentiated sequence. E²C splits reasoning into two explicit phases inside one model:
+
+- **Exploration** (`<EXPLORATION>...</EXPLORATION>`): a short, stochastic plan that outlines the solution strategy (~500 tokens).
+- **Execution** (`<EXECUTION>...</EXECUTION>`): a deterministic, step-by-step derivation that follows the plan exactly.
+
+The two phases are trained jointly. A causal SFT stage teaches the model the E²C format; a two-stage GRPO stage then amplifies the gradient weight on exploration tokens (λ > 1) to sharpen planning while keeping execution deterministic.
+
+## Model variants
+
+| Name | Base | Training |
+|------|------|----------|
+| `8B-Final` | Qwen3-8B | E²C-SFT → E²C-RL (Stage 1 + Stage 2) |
+| `4B-Final` | Qwen3-4B | E²C-SFT → E²C-RL (Stage 1 + Stage 2) |
+
+## Performance
+
+**Mathematical reasoning** (Pass@1, 8 samples, Qwen3-8B base):
+
+| Benchmark | Qwen3-8B + GRPO | E²C (SFT+RL) |
+|-----------|----------------|--------------|
+| AIME 2024 | 36.9% | **40.6%** |
+| AIME 2025 | 34.4% | **33.8%** |
+| MATH500   | 88.2% | **87.7%** |
+| AMC 2023  | 79.3% | **80.3%** |
+
+**Test-time scaling on AIME 2024** (K=32):
+
+| Method | Accuracy | Tokens (k) |
+|--------|----------|-----------|
+| Self-Consistency | 50.0% | 86.2 |
+| Tree-of-Thoughts | 50.0% | 71.3 |
+| E²C-ReAct Loop | **53.3%** | **12.4** |
+
+E²C-ReAct Loop reaches higher accuracy than standard TTS methods while using **7× fewer tokens**, by running the search over short exploration plans rather than full chains.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained(
+    "TingheOliver/Explore-Execute-Chain-Qwen",
+    subfolder="8B-Final",
+    torch_dtype="bfloat16",
+    device_map="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "TingheOliver/Explore-Execute-Chain-Qwen",
+    subfolder="8B-Final",
+)
+
+problem = "Find all positive integers n such that n² + 1 divides n³ + 1."
+
+messages = [{"role": "user", "content": problem}]
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+output = model.generate(**inputs, max_new_tokens=4096, temperature=0.7, do_sample=True)
+response = tokenizer.decode(output[0][inputs.input_ids.shape[1]:], skip_special_tokens=False)
+
+# Parse phases
+if "<EXPLORATION>" in response and "<EXECUTION>" in response:
+    exploration = response.split("<EXPLORATION>")[1].split("</EXPLORATION>")[0].strip()
+    execution   = response.split("<EXECUTION>")[1].split("</EXECUTION>")[0].strip()
+    print("Plan:\n", exploration)
+    print("\nSolution:\n", execution)
+else:
+    print(response)
+```
+
+See the [GitHub repository](https://github.com/TingheOliver/Explore-Execute-Chain) for full evaluation scripts and test-time scaling experiments.
+
+## Training details
+
+| Stage | Description |
+|-------|-------------|
+| Causal SFT data | Full solutions distilled into (exploration, execution) pairs; execution conditioned on exploration |
+| E²C-SFT | Standard cross-entropy on structured output (prompt tokens masked) |
+| E²C-RL Stage 1 | GRPO, rollout=32, temp=1.3, 1 epoch — diversifies exploration |
+| E²C-RL Stage 2 | GRPO, rollout=8, temp=1.0, adv_coeff=2.0, 2 epochs — sharpens execution determinism |
+
+Exploration tokens receive λ-amplified gradient weight throughout RL training to focus the policy improvement signal on the planning phase.
+
+## Citation
+
+```bibtex
+@misc{yang2025e2c,
+  title     = {Explore-Execute Chain: Towards an Efficient Structured Reasoning Paradigm},
+  author    = {Kaisen Yang and Tinghe Zhang and Rushi Shah and Kaicheng Yang and
+               Qinwei Ma and Dianbo Liu and Alex Lamb},
+  year      = {2025},
+  eprint    = {2509.23946},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.LG},
+  url       = {https://arxiv.org/abs/2509.23946}
+}
+```