mlsanityData to 14 Oct 2026

Learning to Ask: Decision Transformers for Adaptive Quantitative Group Testing

arXiv 2509.01723 ↗First posted 1 Sep 2025cs.IT cs.LG math.ITcited by 0

On the map: Offline RL and imitation learning from data → Offline RL algorithms

Abstract (arXiv)

We consider the problem of quantitative group testing (QGT), where the goal is to recover a sparse binary vector from aggregate subset-sum queries: each query selects a subset of indices and returns the sum of those entries. Information-theoretic results suggest that adaptivity could yield up to a twofold reduction in the total number of required queries, yet no algorithm has surpassed the non-adaptive bound, leaving its practical benefit an open question. In this paper, we reduce the QGT problem to an integer-vector recovery task whose dimension scales with the sparsity of the original problem rather than its full ambient size. We then formulate this reduced recovery task as an offline reinforcement learning problem and employ Decision Transformers to solve it adaptively. By combining these two steps, we obtain an effective end-to-end method for solving the QGT problem. Our experiments show that, for the first time in the literature, our adaptive algorithm reduces the average number of queries below the well-known non-adaptive information-theoretic bound, demonstrating that adaptivity can indeed reduce the number of queries.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Offline RL algorithms, one of 13 directions in Offline RL and imitation learning from data. Builds on 10 of its 28 references that are in the map. First posted on arXiv 1 Sep 2025.

Builds on

10 of 28 references.

References · 10

  1. 2017Attention Is All You Needcited by 60,361
  2. 2022RT-1: Robotics Transformer for Real-World Control at Scalecited by 2,061
  3. 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,546
  4. 2021Offline Reinforcement Learning as One Big Sequence Modeling Problemcited by 644
  5. 2022Multi-Game Decision Transformerscited by 214
  6. 2022Prompting Decision Transformer for Few-Shot Policy Generalizationcited by 146
  7. 2021Support Recovery in Universal One-bit Compressed Sensingcited by 6
  8. 2014Optimal Nested Test Plan for Combinatorial Quantitative Group Testingcited by 4
  9. 2023Approximate Message Passing with Rigorous Guarantees for Pooled Data and Quantitative Group Testingcited by 3
  10. 2022Improved Support Recovery in Universal One-bit Compressed Sensingcited by 2

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