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Dataset · Hugging Face

WaitHZ/SSPO-data

SSPO Beyond Outcome Rewards: Step-Level Self-Distilled Policy Optimization for Deep Search Agents 📄 arXiv • 💻 Code • 🤗 Dataset 🌟Overview Deep search agents operate over trajectories spanning dozens of information-seeking steps, but standard reinforcement learning provides only a single outcome reward for the entire trajectory. This sparse signal makes it difficult to determine which intermediate reasoning and tool-use actions should be reinforced… See the full description on the dataset page: https://huggingface.co/datasets/WaitHZ/SSPO-data.

Author / organizationWaitHZ
License (as declared)mit
Downloads357 (as reported by Hugging Face)
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Created6 August 2026
Last updated at source18 September 2026
deep-searchweb-searchweb-agentsreinforcement-learningrlvrreasoningtool-usesfttrajectories
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