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Model: Jincenzi/SocialR1-4B
Source: Original Platform

.gitattributes

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README.md

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+---
+license: mit
+language:
+- en
+base_model:
+- Qwen/Qwen3-4B
+tags:
+- social-reasoning
+- Theory of Mind
+- reinforcement-learning
+- GRPO
+- SIP
+datasets:
+- Jincenzi/ToMBench_Hard
+pipeline_tag: text-generation
+---
+
+# SocialR1-4B
+
+**SocialR1-4B** is a social reasoning model built on [Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B), trained with trajectory-level reinforcement learning (GRPO) using the **Social-R1** framework. It enhances  social reasoning capabilities by aligning reasoning processes with the Social Information Processing (SIP) theory.
+
+📄 **Paper**: [Social-R1: Enhancing Social Reasoning in LLMs through Trajectory-Level Reinforcement Learning](https://arxiv.org/abs/2603.09249)
+
+## Highlights
+
+- 🧠 **SIP-Guided Reasoning**: Enforces stage-consistent social inference — Cue Encoding → Cue Interpretation → Goal Clarification → Response Generation
+- 🎯 **Multi-Dimensional Reward**: Combines structural reward, content reward, inference efficiency, and format reward with curriculum-style weighting
+- 📊 **Strong Performance**: Enables a 4B-parameter model to match or outperform substantially larger baselines across static MCQ benchmarks, open-ended generation (FanToM), and interactive settings (SOTOPIA)
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "Jincenzi/SocialR1-4B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
+
+messages = [
+    {"role": "user", "content": "You should first think about the reasoning process in the mind and then provide with the answer.The reasoning process and answer are enclosed within <think> </think> and <Answer> </Answer> tags, respectively."}
+]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=2048)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Training Details
+
+- **Base Model**: Qwen3-4B
+- **Training Method**: Group Relative Policy Optimization (GRPO)
+- **Training Steps**: 600
+- **Hardware**: 8× NVIDIA A100 (80GB)
+- **Group Size**: 5
+- **KL Coefficient**: 0.04
+- **Learning Rate**: 5×10⁻⁷
+- **Reward Design**: SIP structural reward ($R_\text{struct}$) + SIP content reward ($R_\text{cont}$) + inference efficiency ($R_\text{len}$) + format reward ($R_\text{fmt}$)
+
+## Evaluation
+
+SocialR1-4B is evaluated across three complementary settings:
+
+- **Static MCQ**: ToMBench, ToMBench-Hard, SocialIQA, SimpleToM, EmoBench, MotiveBench, Hi-ToM, TactfulToM
+- **Open-ended Generation**: FanToM
+- **Interactive Social Intelligence**: SOTOPIA
+
+## Related Resources
+
+| Resource | Link |
+|----------|------|
+| Paper | [arXiv:2603.09249](https://arxiv.org/abs/2603.09249) |
+| SocialR1-8B | [Jincenzi/SocialR1-8B](https://huggingface.co/Jincenzi/SocialR1-8B) | 
+
+## Citation
+
+```BibTeX
+@inproceedings{wu2026socialr1,
+  title={Social-R1: Enhancing Social Reasoning in LLMs through Trajectory-Level Reinforcement Learning},
+  author={Wu, Jincenzi and Lei, Yuxuan and Lian, Jianxun and Huang, Yitian and Zhou, Lexin and Li, Haotian and Yang, Deng and Xie, Xing and Meng, Helen},
+  booktitle={Arxiv},
+  year={2026}
+}
+```
+
+## Contact
+
+For questions or discussions, please contact [jincenziwu@gmail.com](mailto:jincenziwu@gmail.com).

added_tokens.json

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+{
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+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
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+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
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+    "full_attention",
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+    "full_attention",
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.6",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+  "do_sample": true,
+  "eos_token_id": [
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+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.57.6"
+}

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special_tokens_map.json

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tokenizer_config.json

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