Papers

• R1-Code-Interpreter: LLMs Reason with Code via Supervised and Multi-stage Reinforcement Learning (2025)
  Yongchao Chen et al.
  4.42
• FormalAnalyticGeo: A Neural-Symbolic Based Framework for Multimodal Analytic Geometry Problem Generation (2026)
  Ruoran Xu et al.
  2.00
• Scheduling That Speaks: An Interpretable Programmatic Reinforcement Learning Framework (2026)
  Chengpeng Hu et al.
  1.89
• Reinforcement Learning for Power-Flow Network Analysis (2026)
  Alperen Ergur et al.
  1.78
• Learning Fast Monomial Orders for Gr\"obner Basis Computations (2026)
  R. Caleb Bunch et al.
  1.72
• NL2CA: Auto-formalizing Cognitive Decision-Making from Natural Language Using an Unsupervised CriticNL2LTL Framework (2025)
  Zihao Deng et al.
  1.61
• Handling Infinite Domain Parameters in Planning Through Best-First Search with Delayed Partial Expansions (2025)
  \'Angel Aso-Mollar and Diego Aineto and Enrico Scala and Eva Onaindia
  1.44
• Deep Symbolic Optimization: Reinforcement Learning for Symbolic Mathematics (2025)
  Conor F. Hayes et al.
  1.22
• FORM: Learning Expressive and Transferable First-Order Logic Reward Machines (2025)
  Leo Ardon et al.
  1.00
• Neural Optimizer Search with Reinforcement Learning (2017)
  Irwan Bello et al.
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• Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization (2022)
  Minsu Kim et al.
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• A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential Decision Making (2023)
  Carlos N\'u\~nez-Molina et al.
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• Deep Inductive Logic Programming meets Reinforcement Learning (2023)
  Andreas Bueff (University of Edinburgh) et al.
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• Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents (2024)
  Quentin Delfosse et al.
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• Constraint-Generation Policy Optimization (CGPO): Nonlinear Programming for Policy Optimization in Mixed Discrete-Continuous MDPs (2024)
  Michael Gimelfarb et al.
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• Towards a Research Community in Interpretable Reinforcement Learning: the InterpPol Workshop (2024)
  Hector Kohler et al.
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