Test-Time Scaling with Reflective Generative Model
We introduce our first reflective generative model MetaStone-S1, which
obtains OpenAI o3's performance via the self-supervised process reward model
(SPRM). Through sharing the backbone network and using task-specific heads for
next token prediction and process scoring respectively, SPRM successfully
integrates the policy model and process reward model(PRM) into a unified
interface without extra process annotation, reducing over 99% PRM parameters
for efficient reasoning. Equipped with SPRM, MetaStone-S1 is naturally suitable
for test time scaling (TTS), and we provide three reasoning effort modes (low,
medium, and high), based on the controllable thinking length. Moreover, we
empirically establish a scaling law that reveals the relationship between total
thinking computation and TTS performance. Experiments demonstrate that our
MetaStone-S1 achieves comparable performance to OpenAI-o3-mini's series with
only 32B parameter size. To support the research community, we have
open-sourced MetaStone-S1 at https://github.com/MetaStone-AI/MetaStone-S1.
6868daac213f123a1f88b9c8
Zixiao Wang
6868daac213f123a1f88b9c9
Yuxin Wang
claimed_verified
2025-07-16T08:16:00.244Z
638700c723da90491eb72722
/avatars/6dcddca5c31121b60e45aab0816e11be.svg
Yuxin Wang
user
wangyuxin87
6868daac213f123a1f88b9ca
Xiaorui Wang
6868daac213f123a1f88b9cb
Mengting Xing
6868daac213f123a1f88b9cc
Jie Gao
6868daac213f123a1f88b9cd
Jianjun Xu
claimed_verified
2025-07-15T19:11:32.070Z
650d13f241521dec20fa7725
/avatars/14897818e5f2232f52e1ca04ac4525ae.svg
JIANJUN XU
user
KAICLIFE
6868daac213f123a1f88b9ce
Guangcan Liu
6868daac213f123a1f88b9cf
Chenhui Jin
6868daac213f123a1f88b9d0
Zhuo Wang
6868daac213f123a1f88b9d1
Shengzhuo Zhang
6868daac213f123a1f88b9d2
Hongtao Xie
Test-Time Scaling with Reflective Generative Model
We introduce our first reflective generative model MetaStone-S1, which
obtains OpenAI o3's performance via the self-supervised process reward model
(SPRM). Through sharing the backbone network and using task-specific heads for
next token prediction and process scoring respectively, SPRM successfully
integrates the policy model and process reward model(PRM) into a unified
interface without extra process annotation, reducing over 99% PRM parameters
for efficient reasoning. Equipped with SPRM, MetaStone-S1 is naturally suitable
for test time scaling (TTS), and we provide three reasoning effort modes (low,
medium, and high), based on the controllable thinking length. Moreover, we
empirically establish a scaling law that reveals the relationship between total
thinking computation and TTS performance. Experiments demonstrate that our
MetaStone-S1 achieves comparable performance to OpenAI-o3-mini's series with
only 32B parameter size. To support the research community, we have
open-sourced MetaStone-S1 at https://github.com/MetaStone-AI/MetaStone-S1.
MetaStone-S1, a reflective generative model using a self-supervised process reward model, achieves high performance with reduced parameters and supports test time scaling.
reflective generative model self-supervised process reward model backbone network task-specific heads policy model process reward model test time scaling reasoning effort modes scaling law total thinking computation TTS performance