9 papers
CoSearch: Joint Training of Reasoning and Document Ranking via Reinforcement Learning for Agentic Search
Hansi Zeng, Liam Collins, Bhuvesh Kumar +2
Agentic search -- the task of training agents that iteratively reason, issue queries, and synthesize retrieved information to answer complex questions -- has achieved remarkable pr…
TARSE: Test-Time Adaptation via Retrieval of Skills and Experience for Reasoning Agents
Junda Wang, Zonghai Tao, Hansi Zeng +3
Complex clinical decision making often fails not because a model lacks facts, but because it cannot reliably select and apply the right procedural knowledge and the right prior exa…
Scaling Laws for Embedding Dimension in Information Retrieval
Julian Killingback, Mahta Rafiee, Madine Manas +1
Dense retrieval, which encodes queries and documents into a single dense vector, has become the dominant neural retrieval approach due to its simplicity and compatibility with fast…
Benchmarking Information Retrieval Models on Complex Retrieval Tasks
Julian Killingback, Hamed Zamani
Large language models (LLMs) are incredible and versatile tools for text-based tasks that have enabled countless, previously unimaginable, applications. Retrieval models, in contra…
Can Pre-training Indicators Reliably Predict Fine-tuning Outcomes of LLMs?
Hansi Zeng, Kai Hui, Honglei Zhuang +4
While metrics available during pre-training, such as perplexity, correlate well with model performance at scaling-laws studies, their predictive capacities at a fixed model size re…
Distillation and Refinement of Reasoning in Small Language Models for Document Re-ranking
Chris Samarinas, Hamed Zamani
We present a novel approach for training small language models for reasoning-intensive document ranking that combines knowledge distillation with reinforcement learning optimizatio…