CODA: Coordinating the Cerebrum and Cerebellum for a Dual-Brain Computer
  Use Agent with Decoupled Reinforcement Learning
Autonomous agents for Graphical User Interfaces (GUIs) face significant
challenges in specialized domains such as scientific computing, where both
long-horizon planning and precise execution are required. Existing approaches
suffer from a trade-off: generalist agents excel at planning but perform poorly
in execution, while specialized agents demonstrate the opposite weakness.
Recent compositional frameworks attempt to bridge this gap by combining a
planner and an actor, but they are typically static and non-trainable, which
prevents adaptation from experience. This is a critical limitation given the
scarcity of high-quality data in scientific domains. To address these
limitations, we introduce CODA, a novel and trainable compositional framework
that integrates a generalist planner (Cerebrum) with a specialist executor
(Cerebellum), trained via a dedicated two-stage pipeline. In the first stage,
Specialization, we apply a decoupled GRPO approach to train an expert planner
for each scientific application individually, bootstrapping from a small set of
task trajectories. In the second stage, Generalization, we aggregate all
successful trajectories from the specialized experts to build a consolidated
dataset, which is then used for supervised fine-tuning of the final planner.
This equips CODA with both robust execution and cross-domain generalization.
Evaluated on four challenging applications from the ScienceBoard benchmark,
CODA significantly outperforms baselines and establishes a new state of the art
among open-source models.
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Zeyi Sun
claimed_verified
2025-08-28T08:54:59.726Z
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Zeyi Sun
user
Zery
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Yuhang Cao
68afc826245176306494cbef
Jianze Liang
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Qiushi Sun
claimed_verified
2025-08-29T08:03:06.940Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1617207525789-noauth.png
Qiushi
user
QiushiSun
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Ziyu Liu
68afc826245176306494cbf2
Zhixiong Zhang
claimed_verified
2025-08-28T08:54:53.634Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/65ab5332043d53781a115475/UaxSFDWteYsByzx7G_KKy.jpeg
Zhixiong Zhang
user
rookiexiong
68afc826245176306494cbf3
Yuhang Zang
claimed_verified
2025-08-28T08:54:56.689Z
63859cf3b2906edaf83af9f0
https://cdn-avatars.huggingface.co/v1/production/uploads/63859cf3b2906edaf83af9f0/kajwuVzd4pDucSPlwghxo.png
Yuhang Zang
user
yuhangzang
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Xiaoyi Dong
68afc826245176306494cbf5
Kai Chen
68afc826245176306494cbf6
Dahua Lin
68afc826245176306494cbf7
Jiaqi Wang
CODA: Coordinating the Cerebrum and Cerebellum for a Dual-Brain Computer
  Use Agent with Decoupled Reinforcement Learning
Autonomous agents for Graphical User Interfaces (GUIs) face significant
challenges in specialized domains such as scientific computing, where both
long-horizon planning and precise execution are required. Existing approaches
suffer from a trade-off: generalist agents excel at planning but perform poorly
in execution, while specialized agents demonstrate the opposite weakness.
Recent compositional frameworks attempt to bridge this gap by combining a
planner and an actor, but they are typically static and non-trainable, which
prevents adaptation from experience. This is a critical limitation given the
scarcity of high-quality data in scientific domains. To address these
limitations, we introduce CODA, a novel and trainable compositional framework
that integrates a generalist planner (Cerebrum) with a specialist executor
(Cerebellum), trained via a dedicated two-stage pipeline. In the first stage,
Specialization, we apply a decoupled GRPO approach to train an expert planner
for each scientific application individually, bootstrapping from a small set of
task trajectories. In the second stage, Generalization, we aggregate all
successful trajectories from the specialized experts to build a consolidated
dataset, which is then used for supervised fine-tuning of the final planner.
This equips CODA with both robust execution and cross-domain generalization.
Evaluated on four challenging applications from the ScienceBoard benchmark,
CODA significantly outperforms baselines and establishes a new state of the art
among open-source models.
CODA, a trainable compositional framework, combines a generalist planner and specialist executor to achieve robust execution and cross-domain generalization in scientific computing GUIs.
compositional frameworks planner actor long-horizon planning precise execution GRPO ScienceBoard benchmark