CRISP

CRISP: Compressed Reasoning via Iterative Self-Policy Distillation

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 is the model with no instruction. Training minimizes per-token reverse KL from student to teacher on the student's own rollouts, with the teacher periodically refreshed (interval M=50). No ground-truth answers, no token budgets, and no difficulty estimators enter the loss.

This repository hosts the step-99 checkpoints for three model families, each trained with two conciseness-instruction variants:

• v1 (uniform): "Solve concisely and correctly. Be direct — avoid unnecessary elaboration, redundant steps, or restating the problem." Compresses most aggressively.
• v2 (difficulty-aware, default): adds a caveat to not over-compress hard/multi-step problems (keep case analysis, edge cases, a final check). Best accuracy preservation.

Checkpoints

Training data: pb09204048/CRISP (DAPO-Math-17k with the v1/v2 conciseness instruction column).

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "pb09204048/CRISP"
sub = "checkpoints/14b_v1"           # pick any checkpoint folder
tok = AutoTokenizer.from_pretrained(repo, subfolder=sub)
model = AutoModelForCausalLM.from_pretrained(repo, subfolder=sub, device_map="auto")

Benchmark results

All numbers use a 30K-token budget and report accuracy (mean@8, %) and token reduction (Red., % vs. the base model). Math (MATH-500, AIME 2024/2025) is scored with a dual-path grader (Answer: or \boxed{}); GPQA-Diamond and MMLU use exact letter-match. Rows: base model, the conciseness prompt at inference only (no training) for v1/v2, and CRISP (trained) for v1/v2.

Qwen3-8B

Qwen3-14B

DeepSeek-R1-Distill-Llama-8B

Takeaways. CRISP compresses reasoning traces substantially while preserving — and often improving — accuracy. The v1 (uniform) teacher compresses ~1.7–2× harder than v2 at a small accuracy cost; v2 best preserves accuracy. On DeepSeek, CRISP raises accuracy on every benchmark.

Citation

@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}
}