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Michael Tang

4 accepted papers

2025

A Single Goal is All You Need: Skills and Exploration Emerge from Contrastive RL without Rewards, Demonstrations, or Subgoals

ICLR 2025poster

In this paper, we present empirical evidence of skills and directed exploration emerging from a simple RL algorithm long before any successful trials are observed. For example, in a manipulation task, the agent is given a single observation of the goal state (see Fig. 1) and learns skills, first for…

Cited by 1SourcePDFScholar
2025

BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval

ICLR 2025spotlight

Existing retrieval benchmarks primarily consist of information-seeking queries (e.g., aggregated questions from search engines) where keyword or semantic-based retrieval is usually sufficient. However, many complex real-world queries require in-depth reasoning to identify relevant documents that go…

Cited by 24SourcePDFScholar
2024

Referral Augmentation for Zero-Shot Information Retrieval

ACL 2024findings

We propose Referral-Augmented Retrieval (RAR), a simple technique that concatenates document indices with referrals: text from other documents that cite or link to the given document. We find that RAR provides significant performance gains for tasks across paper retrieval, entity retrieval, and open…

2019

Atlas of Digital Pathology: A Generalized Hierarchical Histological Tissue Type-Annotated Database for Deep Learning

CVPR 2019poster

In recent years, computer vision techniques have made large advances in image recognition and been applied to aid radiological diagnosis. Computational pathology aims to develop similar tools for aiding pathologists in diagnosing digitized histopathological slides, which would improve diagnostic acc…

Cited by 73PDFcodeScholar