mlsanityData to 14 Oct 2026

Learning Program Synthesis for Integer Sequences from Scratch

arXiv 2202.11908 ↗First posted 24 Feb 2022cs.AI cs.SCcited by 8

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Abstract (arXiv)

We present a self-learning approach for synthesizing programs from integer sequences. Our method relies on a tree search guided by a learned policy. Our system is tested on the On-Line Encyclopedia of Integer Sequences. There, it discovers, on its own, solutions for 27987 sequences starting from basic operators and without human-written training examples.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 4 of its 19 references that are in the map. First posted on arXiv 24 Feb 2022.

Builds on

4 of 19 references.

References · 4

  1. 2017Hindsight Experience Replaycited by 1,136
  2. 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,026
  3. 2022Deep Symbolic Regression for Recurrent Sequencescited by 54
  4. 2020Tree Neural Networks in HOL4cited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 3; 2026: 1.

8 papers.

  1. 2024CodeIt: Self-Improving Language Models with Prioritized Hindsight Replaycited by 23
  2. 2025IMProofBench: Benchmarking AI on Research-Level Mathematical Proof Generationcited by 13
  3. 2023Alien Codingcited by 9
  4. 2023MizAR 60 for Mizar 50cited by 6
  5. 2025Learning Conjecturing from Scratchcited by 3
  6. 2025A Llama walks into the 'Bar': Efficient Supervised Fine-Tuning for Legal Reasoning in the Multi-state Bar Examcited by 3
  7. 2026IntSeqBERT: Learning Arithmetic Structure in OEIS via Modulo-Spectrum Embeddingscited by 1
  8. 2024Artifical intelligence and inherent mathematical difficultycited by 0