Quark-LLM/SSP
Search Self-Play (SSP) Dataset Paper | arXiv | Code Search Self-Play (SSP) is a reinforcement learning framework designed for training adversarial self-play agents with integrated search capabilities—enabling both proposer and solver agents to conduct multi-turn search engine calling and reasoning in a coordinated manner. Through RL training with rule-based outcome rewards, SSP enables two roles to co-evolve in an adversarial competition: the proposer learns to generate… See the full description on the dataset page: https://huggingface.co/datasets/Quark-LLM/SSP.
| Author / organization | Quark-LLM |
|---|---|
| License (as declared) | apache-2.0 |
| Downloads | 206 (as reported by Hugging Face) |
| Likes | 1 |
| Created | 10 November 2025 |
| Last updated at source | 31 December 2025 |
Recent changes
- 16 Oct 2026 Description: Search Self-Play (SSP) Dataset Paper | arXiv | Code Search Self-Play (SSP) is a reinforcement learning framework designed for training adversarial self-play agents with integrated search capabilities—enabling both proposer and solver agents → Search Self-Play (SSP) Dataset Paper | arXiv | Code Search Self-Play (SSP) is a reinforcement learning framework designed for training adversarial self-play agents with integrated search capabilities—enabling both proposer and solver agents
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