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---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
language:
- en
tags:
- math
- reasoning
- reasoning-compression
- self-distillation
- crisp
base_model:
- Qwen/Qwen3-8B
datasets:
- pb09204048/CRISP
---
# CRISP-Qwen3-8B-v2
**Qwen3-8B** trained with **CRISP** (Compressed Reasoning via Iterative Self-Policy Distillation)
using the **v2** conciseness teacher. Step-99 checkpoint.
**Paper:** https://arxiv.org/abs/2603.05433
CRISP teaches a reasoning model to think concisely by distilling its own concise behavior back into
itself: the teacher is the *same* model conditioned on a conciseness instruction, the student has no
instruction, and training minimizes per-token reverse KL from student to teacher on the student's own
rollouts (teacher refreshed every `M=50` steps). No ground-truth answers, token budgets, or
difficulty estimators enter the loss.
This checkpoint uses the **v2** teacher prompt:
**v2 (difficulty-aware, default):** adds a caveat to *not over-compress* hard/multi-step problems (keep case analysis, edge cases, a final check).
Other CRISP checkpoints: [Qwen3-8B](https://huggingface.co/pb09204048/CRISP-Qwen3-8B-v1) ([v2](https://huggingface.co/pb09204048/CRISP-Qwen3-8B-v2)),
[Qwen3-14B](https://huggingface.co/pb09204048/CRISP-Qwen3-14B-v1) ([v2](https://huggingface.co/pb09204048/CRISP-Qwen3-14B-v2)),
[DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/pb09204048/CRISP-DeepSeek-R1-Distill-Llama-8B-v1) ([v2](https://huggingface.co/pb09204048/CRISP-DeepSeek-R1-Distill-Llama-8B-v2)).
Training data: [pb09204048/CRISP](https://huggingface.co/datasets/pb09204048/CRISP).
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("pb09204048/CRISP-Qwen3-8B-v2")
model = AutoModelForCausalLM.from_pretrained("pb09204048/CRISP-Qwen3-8B-v2", device_map="auto")
```
## Benchmark results (Qwen3-8B)
Accuracy (mean@8, %) and token reduction (Red., % vs. base) at a 30K-token budget. Math is scored with
a dual-path grader (`Answer:` or `\boxed{}`); GPQA-Diamond and MMLU use exact letter-match. This
model is the **CRISP (v2)** row.
| Setting | MATH-500 | AIME 2024 | AIME 2025 | GPQA-D | MMLU |
|---------|----------|-----------|-----------|--------|------|
| Base | 95.7 / — | 76.2 / — | 70.4 / — | 61.5 / — | 81.9 / — |
| Concise prompt (v2) | 94.2 / 21.5% | 74.6 / 9.8% | 63.7 / 5.3% | 59.5 / 30.7% | 82.8 / 26.8% |
| Concise prompt (v1) | 95.6 / 38.9% | 74.2 / 20.2% | 62.1 / 13.9% | 56.8 / 29.5% | 83.0 / 26.8% |
| **CRISP (v2)** | 95.7 / 31.6% | 75.0 / 17.1% | 65.8 / 17.5% | 58.3 / 17.2% | 81.2 / 22.4% |
| **CRISP (v1)** | 95.7 / 56.9% | 72.9 / 32.9% | 58.8 / 28.4% | 58.5 / 36.2% | 80.9 / 44.7% |
## Citation
```bibtex
@article{sang2026crisp,
title={Crisp: Compressed reasoning via iterative self-policy distillation},
author={Sang, Hejian and Xu, Yuanda and Zhou, Zhengze and He, Ran and Wang, Zhipeng and Sun, Jiachen},
journal={arXiv preprint arXiv:2603.05433},
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 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) %}
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{{- '<|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 %}
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{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- 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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