Cycle-Consistent Search: Question Reconstructability as a Proxy Reward for Search Agent Training
📰 ArXiv cs.AI
Learn how to train search agents without gold supervision using Cycle-Consistent Search, a novel framework that leverages question reconstructability as a proxy reward
Action Steps
- Implement cycle-consistency techniques from unsupervised machine translation to search agent training
- Use question reconstructability as a proxy reward to optimize search agents
- Train search agents using reinforcement learning without relying on gold supervision
- Evaluate the performance of search agents using metrics such as precision and recall
- Apply Cycle-Consistent Search to complex information retrieval tasks to improve search results
Who Needs to Know This
Researchers and engineers working on information retrieval and reinforcement learning can benefit from this approach to improve search agent training without relying on ground-truth answers
Key Insight
💡 Question reconstructability can be used as a proxy reward for search agent training, eliminating the need for ground-truth answers
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🚀 Introducing Cycle-Consistent Search: a novel framework for training search agents without gold supervision! 🤖
Key Takeaways
Learn how to train search agents without gold supervision using Cycle-Consistent Search, a novel framework that leverages question reconstructability as a proxy reward
Full Article
Title: Cycle-Consistent Search: Question Reconstructability as a Proxy Reward for Search Agent Training
Abstract:
arXiv:2604.12967v1 Announce Type: new Abstract: Reinforcement Learning (RL) has shown strong potential for optimizing search agents in complex information retrieval tasks. However, existing approaches predominantly rely on gold supervision, such as ground-truth answers, which is difficult to scale. To address this limitation, we propose Cycle-Consistent Search (CCS), a gold-supervision-free framework for training search agents, inspired by cycle-consistency techniques from unsupervised machine t
Abstract:
arXiv:2604.12967v1 Announce Type: new Abstract: Reinforcement Learning (RL) has shown strong potential for optimizing search agents in complex information retrieval tasks. However, existing approaches predominantly rely on gold supervision, such as ground-truth answers, which is difficult to scale. To address this limitation, we propose Cycle-Consistent Search (CCS), a gold-supervision-free framework for training search agents, inspired by cycle-consistency techniques from unsupervised machine t
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