SSL: Sweet Spot Learning for Differentiated Guidance in Agentic Optimization
Reinforcement learning with verifiable rewards has emerged as a powerful paradigm for training intelligent agents. However, existing methods typically employ binary rewards that fail to capture quality differences among trajectories achieving identical outcomes, thereby overlooking potential diversity within the solution space. Inspired by the ``sweet spot'' concept in tennis-the racket's core region that produces optimal hitting effects, we introduce Sweet Spot Learning (SSL), a novel framework that provides differentiated guidance for agent optimization. SSL follows a simple yet effective principle: progressively amplified, tiered rewards guide policies toward the sweet-spot region of the solution space. This principle naturally adapts across diverse tasks: visual perception tasks leverage distance-tiered modeling to reward proximity, while complex reasoning tasks reward incremental progress toward promising solutions. We theoretically demonstrate that SSL preserves optimal solution ordering and enhances the gradient signal-to-noise ratio, thereby fostering more directed optimization. Extensive experiments across GUI perception, short/long-term planning, and complex reasoning tasks show consistent improvements over strong baselines on 12 benchmarks, achieving up to 2.5X sample efficiency gains and effective cross-task transferability. Our work establishes SSL as a general principle for training capable and robust agents.
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Jinyang Wu
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Changpeng Yang
claimed_verified
2026-02-02T16:52:51.129Z
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Changpeng Yang
thkelper
user
thkelper
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Yuhao Shen
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Fangzhi Xu
claimed_verified
2026-02-06T18:56:33.410Z
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Fangzhi Xu
xufangzhi
user
xufangzhi
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Bolin Ni
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Chonghua Liao
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Yuchen Liu
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Hongzhen Wang
69801f416676f933227065ae
Shuai Nie
69801f416676f933227065af
Shuai Zhang
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Haoran Luo
69801f416676f933227065b1
Jiaming Xu
SSL: Sweet Spot Learning for Differentiated Guidance in Agentic Optimization
Reinforcement learning with verifiable rewards has emerged as a powerful paradigm for training intelligent agents. However, existing methods typically employ binary rewards that fail to capture quality differences among trajectories achieving identical outcomes, thereby overlooking potential diversity within the solution space. Inspired by the ``sweet spot'' concept in tennis-the racket's core region that produces optimal hitting effects, we introduce Sweet Spot Learning (SSL), a novel framework that provides differentiated guidance for agent optimization. SSL follows a simple yet effective principle: progressively amplified, tiered rewards guide policies toward the sweet-spot region of the solution space. This principle naturally adapts across diverse tasks: visual perception tasks leverage distance-tiered modeling to reward proximity, while complex reasoning tasks reward incremental progress toward promising solutions. We theoretically demonstrate that SSL preserves optimal solution ordering and enhances the gradient signal-to-noise ratio, thereby fostering more directed optimization. Extensive experiments across GUI perception, short/long-term planning, and complex reasoning tasks show consistent improvements over strong baselines on 12 benchmarks, achieving up to 2.5X sample efficiency gains and effective cross-task transferability. Our work establishes SSL as a general principle for training capable and robust agents.
Sweet Spot Learning (SSL) introduces a novel reinforcement learning framework that uses tiered rewards to guide agent optimization toward optimal regions of the solution space, improving sample efficiency and cross-task transferability.
reinforcement learning verifiable rewards trajectory optimization policy optimization gradient signal-to-noise ratio sample efficiency cross-task transferability