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Model: xx18/Composition-RL-4B
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---
library_name: transformers
pipeline_tag: text-generation
---
# Composition-RL-8B
This repository contains the **Composition-RL-8B** model checkpoint, a version of Qwen3-8B-Base fine-tuned using the **Composition-RL** framework.
Composition-RL is a data-efficient Reinforcement Learning with Verifiable Rewards (RLVR) approach introduced in the paper: [Composition-RL: Compose Your Verifiable Prompts for Reinforcement Learning of Large Language Models](https://huggingface.co/papers/2602.12036).
## Overview
Composition-RL addresses the challenge of "too-easy" prompts (where the pass rate becomes 1 during training) by automatically composing multiple verifiable problems into a single, more complex, yet still verifiable prompt. This ensures that the model continues to receive informative training signals throughout the RL process, leading to improved reasoning capabilities across mathematical and scientific domains.
## Model Details
- **Base Model:** Qwen3-8b-Base
- **Training Set:** [MATH-Composition-199K](https://huggingface.co/datasets/xx18/MATH-Composition-199K)
- **Framework:** Reinforcement Learning with Verifiable Rewards (RLVR)
## Resources
- **Paper:** [Composition-RL: Compose Your Verifiable Prompts for Reinforcement Learning of Large Language Models](https://huggingface.co/papers/2602.12036)
- **GitHub Repository:** [XinXU-USTC/Composition-RL](https://github.com/XinXU-USTC/Composition-RL)
- **Collection:** [Composition-RL Models and Datasets](https://huggingface.co/collections/xx18/composition-rl)
## Usage
For implementation details, including data generation and evaluation scripts, please refer to the [official GitHub repository](https://github.com/XinXU-USTC/Composition-RL).
## Citation
If you find this work helpful for your research, please consider citing:
```bibtex
@article{xu2026composition-rl,
title={Composition-RL: Compose Your Verifiable Prompts for Reinforcement Learning of Large Language Models},
author={Xu, Xin and Bai, Clive and Yang, Kai and Chen, Tianhao and Chen, Yangkun and Liu, Weijie and Chen, Hao and Wang, Yang and Yang, Saiyong and Yang, Can},
journal={arXiv preprint arXiv:2602.12036},
year={2026}
}
```

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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 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.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
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{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in message.content %}
{%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
{%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
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{%- if loop.last or (not loop.last and reasoning_content) %}
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{%- 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") %}
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{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{%- endif %}
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{%- 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 %}

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{
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"transformers_version": "4.54.0.dev0"
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