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 / organization | WaitHZ |
|---|---|
| License (as declared) | mit |
| Downloads | 357 (as reported by Hugging Face) |
| Likes | 1 |
| Created | 6 August 2026 |
| Last updated at source | 18 September 2026 |
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