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Model: julien31/Soar-qwen-7b Source: Original Platform
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---
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base_model:
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- Qwen/Qwen2.5-Coder-7B-Instruct
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datasets:
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- julien31/soar_arc_train_5M
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license: apache-2.0
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pipeline_tag: text-generation
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tags:
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- text-generation
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- code-generation
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- program-synthesis
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- evolutionary-algorithms
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- arc
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- arc-agi
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- soar
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library_name: transformers
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---
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# SOAR-ARC Models: Self-Improving Language Models for Program Synthesis
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<p align="center">
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🤗 <a href="https://huggingface.co/collections/julien31/soar-arc-6856d27681fce01d9af4c4a3">Hugging Face (data and model)</a>   |    📑 <a href="https://huggingface.co/papers/2507.14172">Paper</a>    |    📑 <a href="https://julienp.netlify.app/posts/soar/">Blog</a>
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</p>
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This repository contains one of the models fine-tuned using the **SOAR** (**S**elf-improving **O**perators for **A**utomated program **R**efinements) framework, as presented in the paper:
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> [**Self-Improving Language Models for Evolutionary Program Synthesis: A Case Study on ARC-AGI**](https://huggingface.co/papers/2507.14172)
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>
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> Julien Pourcel, Cédric Colas, Pierre-Yves Oudeyer.
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> *Proceedings of the 42nd International Conference on Machine Learning (ICML), 2025.*
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These models are specialized in solving tasks from the challenging [Abstraction and Reasoning Corpus (ARC)](https://github.com/fchollet/ARC) by synthesizing Python programs.
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## SOAR
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Large Language Models (LLMs) have become incredibly powerful, but they often hit a wall when faced with truly complex reasoning tasks that require discovering a solution from scratch. Simply throwing more computing power or using a bigger model often yields diminishing returns. But what if a model could learn from its own experience, getting smarter with every attempt?
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We introduce a framework called **SOAR (Self-improving Operators for Automated program Refinements)** that does just that. By creating a "virtuous cycle" of evolutionary search and learning, SOAR enables AI models to bootstrap their own capabilities and solve problems previously beyond their reach. we tested SOAR on the Abstraction and Reasoning Corpus (ARC-AGI-1), a notoriously difficult benchmark designed to challenge an AI's core reasoning abilities. We show that using SOAR with only open weight LLM, we can significantly outperforming much larger closed source LLMs.
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We have released a dataset containing 5 million ARC solutions. For solutions that successfully solve an original ARC task, we deduplicate entries by their code to ensure uniqueness. For solutions that correspond to new synthetic tasks generated via hindsight relabeling, we deduplicate based on their output results. This approach ensures a diverse and high-quality dataset for further research and development.
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- [soar_arc_train_5M](https://huggingface.co/datasets/julien31/soar_arc_train_5M)
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We have also released all five of our SOAR models on Hugging Face:
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- [Soar-qwen-7b](https://huggingface.co/julien31/Soar-qwen-7b)
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- [Soar-qwen-14b](https://huggingface.co/julien31/Soar-qwen-14b)
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- [Soar-qwen-32b](https://huggingface.co/julien31/Soar-qwen-32b)
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- [Soar-qwen-72b](https://huggingface.co/julien31/Soar-qwen-72b)
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- [Soar-mistral-123b](https://huggingface.co/julien31/Soar-mistral-123b)
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## SOAR framework
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1. **Evolutionary Search (Sample & Refine):** SOAR uses an LLM to generate an initial pool of thousands of candidate programs (the "sampling" step). It then tests these programs and uses the LLM again to intelligently modify or "refine" the most promising ones based on their performance.
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2. **Learning from Hindsight:** SOAR takes all the programs generated during the search phase—including both successes and failures—and uses them as training data. The key insight is that any failed program is simply a *correct* program for a *different* task. By "relabeling" these failed attempts as correct solutions for the synthetic tasks they inadvertently solve, SOAR creates a diverse dataset to learn from.
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This process creates a powerful feedback loop: the fine-tuned model becomes better at sampling and refining, which leads to a more effective search in the next iteration, which in turn generates even better training data. And unlike previous approaches that rely on human-engineered domain-specific languages or human-generated solutions, SOAR learns to synthesize programs in Python solely from its own synthesis attempts, encompassing both successes and failures.
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## How to Use the Model
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The primary use of this model is to generate a Python function that solves an ARC task. The input to the model should be a formatted prompt containing the training and test examples of the ARC task.
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Here's a quick example to get started:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "julien31/Soar-qwen-7b" # or any other Soar-qwen model
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16, # Use torch.float16 for GPUs that don't support bfloat16
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device_map="auto",
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)
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prompt = "def solve_arc_task(input_grid, output_grid):\
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\\\"\\\"\\\"Given an ARC-AGI task, transform the input grid to the output grid by applying a series of operations.\
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\\\"\\\"\\\""
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messages = [
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer(text, return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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model_inputs.input_ids,
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max_new_tokens=256,
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do_sample=True,
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temperature=0.7,
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top_p=0.8,
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repetition_penalty=1.1,
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.pad_token_id, # This is often the same as eos_token_id for Qwen models
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)
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# Decode only the newly generated text
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decoded_output = tokenizer.decode(generated_ids[0, model_inputs.input_ids.shape[1]:], skip_special_tokens=True)
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print(decoded_output)
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```
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For a complete, end-to-end example of how to format the prompt, run inference, execute the generated code, and visualize the results, please refer to the official repository and notebook:
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* **Official SOAR GitHub Repository**: [https://github.com/flowersteam/SOAR](https://github.com/flowersteam/SOAR)
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* **Inference & Visualization Notebook**: [https://github.com/flowersteam/SOAR/blob/main/notebook/inference_visualisation.ipynb](https://github.com/flowersteam/SOAR/blob/main/notebook/inference_visualisation.ipynb)
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<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made with unsloth.png" width="20%" />
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## Installation
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### Conda inference environment
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```
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pip install --upgrade pip
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git clone https://github.com/flowersteam/SOAR
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cd SOAR
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conda create --name sglang47 \
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python=3.11 \
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-y
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conda activate sglang47
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pip install "sglang[all]>=0.4.7"
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pip install -e .
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pip install -r requirements
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```
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### Conda training environment
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```
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conda create --name unsloth_env \
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python=3.11 \
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pytorch-cuda=12.1 \
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pytorch cudatoolkit xformers -c pytorch -c nvidia -c xformers \
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-y
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conda activate unsloth_env
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pip install unsloth
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cd SOAR
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pip install -e .
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pip install -r requirements.txt
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```
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## Run SOAR
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To run SOAR, please refer to execution instructions located in the experience folder.
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For simple instructions on running sampling and refinement with SOAR, as well as exploring the dataset, please see the Jupyter notebooks provided in the `notebook` folder. These notebooks walk through the basic SOAR step, including how to generate candidate solutions, perform refinement, and analyze results. This hands-on guide will help you get started quickly and understand each step of the SOAR process.
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25
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25
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Normal file
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3
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217
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217
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Normal file
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"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# 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>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\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\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %} {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 32768,
|
||||
"pad_token": "<|PAD_TOKEN|>",
|
||||
"padding_side": "left",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user