PRAISE: Prefix-Based Rollout Reuse in Agentic Search Training
arXiv:2604.03675v1 Announce Type: new Abstract: In agentic search, large language models (LLMs) are trained to perform multi-turn retrieval and reasoning for complex tasks such as multi-hop question answering (QA). However, current search-based Reinforcement Learning (RL) methods suffer from two core limitations: expensive long-horizon rollouts are under-utilized during training, and supervision is typically available only at the final answer, resulting in severe reward sparsity. We present Prefix-based Rollout reuse for Agentic search with Intermediate Step rEwards (PRAISE), a framework for improving both data efficiency and credit assignment in agentic search training. Given a complete search trajectory, PRAISE extracts prefix states at different search turns, elicits intermediate answers from them, and uses these prefixes both to construct additional training trajectories and to derive step-level rewards from performance differences across prefixes. Our method uses a single shared
arXiv:2604.03675v1 Announce Type: new Abstract: In agentic search, large language models (LLMs) are trained to perform multi-turn retrieval and reasoning for complex tasks such as multi-hop question answering (QA). However, current search-based Reinforcement Learning (RL) methods suffer from two core limitations: expensive long-horizon rollouts are under-utilized during training, and supervision is typically available only at the final answer, resulting in severe reward sparsity. We present Prefix-based Rollout reuse for Agentic search with Intermediate Step rEwards (PRAISE), a framework for improving both data efficiency and credit assignment in agentic search training. Given a complete search trajectory, PRAISE extracts prefix states at different search turns, elicits intermediate answers from them, and uses these prefixes both to construct additional training trajectories and to derive step-level rewards from performance differences across prefixes. Our method uses a single shared model for both search policy learning and prefix answer evaluation, enabling joint optimization without extra human annotations or a separate reward model. Experiments on multi-hop QA benchmarks show that PRAISE consistently improves performance over strong baselines.
Executive Summary
The article 'PRAISE: Prefix-Based Rollout Reuse in Agentic Search Training' proposes a novel framework for improving data efficiency and credit assignment in agentic search training. The framework, PRAISE, extracts prefix states from search trajectories, elicits intermediate answers, and uses these prefixes to construct additional training trajectories and derive step-level rewards. Experiments on multi-hop QA benchmarks demonstrate that PRAISE outperforms strong baselines. This article contributes significantly to the field of natural language processing and reinforcement learning, offering a promising solution to challenges in agentic search training. The proposed method's ability to reuse rollouts and provide intermediate rewards enhances the training process, leading to improved performance. This innovation has the potential to revolutionize complex search-based tasks, such as multi-hop question answering.
Key Points
- ▸ PRAISE is a novel framework for improving data efficiency and credit assignment in agentic search training.
- ▸ The framework extracts prefix states from search trajectories and uses them to construct additional training trajectories.
- ▸ PRAISE provides intermediate rewards, enabling step-level credit assignment and improved training efficiency.
Merits
Advancements in Agentic Search Training
PRAISE's ability to reuse rollouts and provide intermediate rewards enhances the training process, leading to improved performance in complex search-based tasks.
Efficient Training and Credit Assignment
The proposed method enables joint optimization of search policy learning and prefix answer evaluation, eliminating the need for extra human annotations or a separate reward model.
Demerits
Dependence on Intermediate Rewards
The effectiveness of PRAISE relies on the availability of intermediate rewards, which may not always be feasible in real-world scenarios.
Potential Overfitting
The use of prefix states and intermediate answers may lead to overfitting if not properly regularized, which could compromise the model's generalizability.
Expert Commentary
The article presents a significant contribution to the field of agentic search training, addressing the challenges of data efficiency and credit assignment. The proposed framework's ability to reuse rollouts and provide intermediate rewards is a major innovation, enhancing the training process and leading to improved performance. However, the article's reliance on intermediate rewards and potential overfitting concerns warrant further investigation. Nevertheless, PRAISE's efficiency gains and ability to inform policy decisions make it a promising solution for complex search-based tasks.
Recommendations
- ✓ Future research should investigate the role of intermediate rewards in PRAISE and explore methods to mitigate overfitting.
- ✓ The proposed framework should be applied to a broader range of search-based tasks to demonstrate its generalizability and potential for real-world applications.
Sources
Original: arXiv - cs.AI