Contrastive Representations for Combinatorial Reasoning

Abstract

Contrastive learning (CL) has emerged as a powerful framework for learning structured representations that enable a wide range of downstream tasks. Its applications span sample-efficient reinforcement learning (RL), retrieval-augmented generation, and improved selection of model-generated samples, among others. Despite these successes, its potential for combinatorial reasoning problems remains largely untapped. In this paper, we take a step in this direction by using temporal contrastive learning to learn representations conducive to solving planning problems, which will reduce our reliance on planning. Our analysis reveals that standard CL approaches struggle to capture temporal dependencies over complex trajectories. To address this, we introduce a novel method that leverages negatives from the same trajectories. Across three complex reasoning tasks, our approach outperforms traditional supervised learning.