PromptCoT 2.0: Scaling Prompt Synthesis for Large Language Model
  Reasoning
Large language models (LLMs) are evolving from conversational systems into
strong reasoners for tasks such as Olympiad mathematics and competitive
programming. While scaling parameters and test-time computation has driven
progress, a key bottleneck is the lack of high-quality training problems:
human-curated datasets are costly and limited, while existing synthetic corpora
are often too easy or narrow. PromptCoT 1.0 showed that injecting rationales
into prompt synthesis increases problem difficulty. Building on this, we
present PromptCoT 2.0, a scalable framework that replaces hand-crafted
heuristics with an expectation-maximization (EM) loop, where rationales are
iteratively refined to guide prompt construction. This produces problems that
are both harder and more diverse than prior corpora. The synthetic prompts
support two post-training regimes: (1) Self-Play, where strong models improve
autonomously via verifiable feedback without stronger teachers; and (2)
Supervised Fine-Tuning (SFT), where weaker models learn from teacher-distilled
traces. Extensive experiments demonstrate the effectiveness of this approach.
In self-play, applying PromptCoT 2.0 to Qwen3-30B-A3B-Thinking-2507 sets new
state-of-the-art results at the 30B scale, with +4.4, +4.8, and +5.3 on AIME
24/25 and HMMT 25, +6.1 and +5.0 on LiveCodeBench v5/v6, and +35 Elo on
Codeforces. In SFT, training Qwen2.5-7B-Instruct solely on synthetic prompts
boosts accuracy to 73.1 (AIME 24), 65.6 (AIME 25), and 53.4 (LiveCodeBench v5),
surpassing models trained on human or hybrid data. Analyses further confirm
that PromptCoT 2.0 yields fundamentally harder and distributionally distinct
problems. These results establish prompt synthesis as a new axis for scaling
reasoning and position PromptCoT 2.0 as a scalable foundation for future
open-source models. The implementation is available at
https://github.com/inclusionAI/PromptCoT.
68d6885d8ccd91bdd39fff60
Xueliang Zhao
claimed_verified
2025-09-29T06:01:33.721Z
66b291a2d4db4314da3f7664
/avatars/3944b62f0be9e1894603bb082c1efc7f.svg
Xueliang Zhao
user
xl-zhao
68d6885d8ccd91bdd39fff61
Wei Wu
68d6885d8ccd91bdd39fff62
Jian Guan
68d6885d8ccd91bdd39fff63
Zhuocheng Gong
68d6885d8ccd91bdd39fff64
Lingpeng Kong
PromptCoT 2.0: Scaling Prompt Synthesis for Large Language Model
  Reasoning
Large language models (LLMs) are evolving from conversational systems into
strong reasoners for tasks such as Olympiad mathematics and competitive
programming. While scaling parameters and test-time computation has driven
progress, a key bottleneck is the lack of high-quality training problems:
human-curated datasets are costly and limited, while existing synthetic corpora
are often too easy or narrow. PromptCoT 1.0 showed that injecting rationales
into prompt synthesis increases problem difficulty. Building on this, we
present PromptCoT 2.0, a scalable framework that replaces hand-crafted
heuristics with an expectation-maximization (EM) loop, where rationales are
iteratively refined to guide prompt construction. This produces problems that
are both harder and more diverse than prior corpora. The synthetic prompts
support two post-training regimes: (1) Self-Play, where strong models improve
autonomously via verifiable feedback without stronger teachers; and (2)
Supervised Fine-Tuning (SFT), where weaker models learn from teacher-distilled
traces. Extensive experiments demonstrate the effectiveness of this approach.
In self-play, applying PromptCoT 2.0 to Qwen3-30B-A3B-Thinking-2507 sets new
state-of-the-art results at the 30B scale, with +4.4, +4.8, and +5.3 on AIME
24/25 and HMMT 25, +6.1 and +5.0 on LiveCodeBench v5/v6, and +35 Elo on
Codeforces. In SFT, training Qwen2.5-7B-Instruct solely on synthetic prompts
boosts accuracy to 73.1 (AIME 24), 65.6 (AIME 25), and 53.4 (LiveCodeBench v5),
surpassing models trained on human or hybrid data. Analyses further confirm
that PromptCoT 2.0 yields fundamentally harder and distributionally distinct
problems. These results establish prompt synthesis as a new axis for scaling
reasoning and position PromptCoT 2.0 as a scalable foundation for future
open-source models. The implementation is available at
https://github.com/inclusionAI/PromptCoT.
PromptCoT 2.0 uses an EM loop to generate harder and more diverse synthetic prompts, improving reasoning capabilities in large language models through self-play and supervised fine-tuning.
LLMs reasoners Olympiad mathematics competitive programming prompt synthesis rationales expectation-maximization (EM) loop self-play supervised fine-tuning (SFT) Qwen3-30B-A3B-Thinking-2507 Qwen2.5-7B-Instruct AIME HMMT LiveCodeBench Codeforces prompt synthesis open-source models