Awesome Formal Languages

Key papers

• Strategy Game-playing With Size-constrained State Abstraction (2024)
  Linjie Xu, Diego Perez-Liebana, Alexander Dockhorn
  5.72
• Demystifying Reinforcement Learning In Agentic Reasoning (2025)
  Zhaochen Yu, Ling Yang, Jiaru Zou, et al.
  5.13
• Simplifying the Modeling of Arbitrary Conditionals in Natural Language (2026)
  Yinhan Lu et al.
  4.39
• A First-Principles Derivation of LLM Policy Optimization: From Expected Reward to GRPO and Its Structural Extensions (2026)
  Jianghan Shen et al.
  4.39
• Does Traversal Order Matter? A Systematic Study of Tree Traversal Methods in Transformer Grammars (2026)
  Zongru Liu et al.
  4.39
• For How Long Should We Be Punching? Learning Action Duration in Fighting Games (2026)
  Hoang Hai Nguyen et al.
  4.33
• STaR: Towards Effective and Stable Table Reasoning via Slow-Thinking Large Language Models (2025)
  Huajian Zhang et al.
  3.21
• Enhancing Table Reasoning with Deterministic Table-State Rewards (2026)
  Tung Sum Thomas Kwok et al.
  2.00
• From Imitation to Interaction: Mastering Game of Schnapsen with Shallow Reinforcement Learning (2026)
  J\'an Kla\v{c}an et al.
  2.00
• TaskGround: Structured Executable Task Inference for Full-Scene Household Reasoning (2026)
  ZhiYuan Feng et al.
  2.00
• Efficiently Representing Algorithms With Chain-of-Thought Transformers (2026)
  Yanhong Li et al.
  2.00
• Manifold Bandits: Bayesian Curriculum Learning over the Latent Geometry of Large Language Models (2026)
  Darrien McKenzie et al.
  2.00
• Beyond Entropy: Learning from Token-Level Distributional Deviations for LLM Reasoning (2026)
  Xuanzhi Feng et al.
  2.00
• What Makes Effective Supervision in Latent Chain-of-Thought: An Information-Theoretic Analysis (2026)
  Xinghao Chen et al.
  2.00
• HER: Human-like Reasoning And Reinforcement Learning For LLM Role-playing (2026)
  Chengyu Du, Xintao Wang, Aili Chen, et al.
  2.00
• Rethinking Entropy Interventions In RLVR: An Entropy Change Perspective (2026)
  Zhezheng Hao, Hong Wang, Haoyang Liu, et al.
  2.00
• Klong: Training LLM Agent For Extremely Long-horizon Tasks (2026)
  Yue Liu, Yingwei Ma, Yibo Miao, et al.
  2.00
• Scheduling Your LLM Reinforcement Learning With Reasoning Trees (2026)
  Hong Wang, Zhezheng Hao, Jian Luo, et al.
  2.00
• Reason Only When Needed: Efficient Generative Reward Modeling Via Model-internal Uncertainty (2026)
  Chao Xue, Yao Wang, Mengqiao Liu, et al.
  2.00
• Hierarchical Alignment: Enforcing Hierarchical Instruction-following In Llms Through Logical Consistency (2026)
  Shu Yang, Zihao Zhou, di Wang, et al.
  2.00
• LoopRPT: Reinforcement Pre-Training for Looped Language Models (2026)
  Guo Tang et al.
  1.83
• Moded Types for Grassroots Logic Programs, by AI, for AI (Full Version) (2026)
  Ehud Shapiro
  1.72
• Provably Correct Automata Embeddings for Optimal Automata-Conditioned Reinforcement Learning (2025)
  Beyazit Yalcinkaya et al.
  1.17
• Adaptive Bi-Level Multi-Robot Task Allocation and Learning under Uncertainty with Temporal Logic Constraints (2025)
  Xiaoshan Lin et al.
  1.11
• Mitigating Information Loss in Tree-Based Reinforcement Learning via Direct Optimization (2024)
  Sascha Marton et al.
  0.78
• Designing Behavior Trees From Goal-oriented Ltlf Formulas (2023)
  Aadesh Neupane, Eric G Mercer, Michael A. Goodrich
  0.00
• Hierarchy Through Composition With Linearly Solvable Markov Decision Processes (2016)
  Andrew M. Saxe, Adam Earle, Benjamin Rosman
  0.00
• Learning Minimally-Violating Continuous Control for Infeasible Linear Temporal Logic Specifications (2022)
  Mingyu Cai et al.
  —
• Goal Space Abstraction in Hierarchical Reinforcement Learning via Reachability Analysis (2023)
  Mehdi Zadem (LIX et al.
  —
• Certifying Robustness of Graph Convolutional Networks for Node Perturbation with Polyhedra Abstract Interpretation (2024)
  Boqi Chen et al.
  —
• Reward Machines for Deep RL in Noisy and Uncertain Environments (2024)
  Andrew C. Li et al.
  —
• Learning Elementary Cellular Automata with Transformers (2024)
  Mikhail Burtsev
  —