Copertina

Rulebook: bringing co-routines to reinforcement learning environments

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Fioravanti, Massimo, Pasini, Samuele, Agosta, Giovanni
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Programming Languages Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

4Hammer: a board-game reinforcement learning environment for the hour long time frame
di: Fioravanti, Massimo, et al.
Pubblicazione: (2025)

Verified Lifting of Deep learning Operators
di: Zhan, Qi, et al.
Pubblicazione: (2024)

Code Simulation Challenges for Large Language Models
di: La Malfa, Emanuele, et al.
Pubblicazione: (2024)

From Reasoning to Code: GRPO Optimization for Underrepresented Languages
di: Pennino, Federico, et al.
Pubblicazione: (2025)

Towards LLM-based optimization compilers. Can LLMs learn how to apply a single peephole optimization? Reasoning is all LLMs need!
di: Fang, Xiangxin, et al.
Pubblicazione: (2024)

Survey on reinforcement learning for language processing
di: Uc-Cetina, Victor, et al.
Pubblicazione: (2021)

A Deep Learning Model for Predicting Transformation Legality
di: Tiwari, Avani, et al.
Pubblicazione: (2025)

A Formally Verified Robustness Certifier for Neural Networks (Extended Version)
di: Tobler, James, et al.
Pubblicazione: (2025)

Small Language Models as Compiler Experts: Auto-Parallelization for Heterogeneous Systems
di: Devadiga, Prathamesh
Pubblicazione: (2025)

Agnostics: Learning to Code in Any Programming Language via Reinforcement with a Universal Learning Environment
di: Boruch-Gruszecki, Aleksander, et al.
Pubblicazione: (2025)

Yggdrasil: Bridging Dynamic Speculation and Static Runtime for Latency-Optimal Tree-Based LLM Decoding
di: Guan, Yue, et al.
Pubblicazione: (2025)

NaN-Propagation: A Novel Method for Sparsity Detection in Black-Box Computational Functions
di: Sharpe, Peter
Pubblicazione: (2025)

Adaptive Branch-and-Bound Tree Exploration for Neural Network Verification
di: Fukuda, Kota, et al.
Pubblicazione: (2025)

Type-Constrained Code Generation with Language Models
di: Mündler, Niels, et al.
Pubblicazione: (2025)

Compiling to recurrent neurons
di: Velez-Ginorio, Joey, et al.
Pubblicazione: (2025)

DeepCircuitX: A Comprehensive Repository-Level Dataset for RTL Code Understanding, Generation, and PPA Analysis
di: Li, Zeju, et al.
Pubblicazione: (2025)

Guarding the Privacy of Label-Only Access to Neural Network Classifiers via iDP Verification
di: Kabaha, Anan, et al.
Pubblicazione: (2025)

RefineStat: Efficient Exploration for Probabilistic Program Synthesis
di: Kanda, Madhav, et al.
Pubblicazione: (2025)

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks
di: Zulkifli, Kahfi S., et al.
Pubblicazione: (2025)

Filter Equivariant Functions: A symmetric account of length-general extrapolation on lists
di: Lewis, Owen, et al.
Pubblicazione: (2025)

Compiling to linear neurons
di: Velez-Ginorio, Joey, et al.
Pubblicazione: (2025)

Technical Report: Full-Stack Fine-Tuning for the Q Programming Language
di: Hogan, Brendan R., et al.
Pubblicazione: (2025)

Representing Molecules with Algebraic Data Types: Beyond SMILES and SELFIES
di: Goldstein, Oliver, et al.
Pubblicazione: (2025)

CRANE: Reasoning with constrained LLM generation
di: Banerjee, Debangshu, et al.
Pubblicazione: (2025)

Cost-Driven Synthesis of Sound Abstract Interpreters
di: Gu, Qiuhan, et al.
Pubblicazione: (2025)

Automated Type Annotation in Python Using Large Language Models
di: Bharti, Varun, et al.
Pubblicazione: (2025)

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs
di: Zhao, Yifan, et al.
Pubblicazione: (2025)

Faster Verified Explanations for Neural Networks
di: De Palma, Alessandro, et al.
Pubblicazione: (2025)

Data-driven Verification of Procedural Programs with Integer Arrays
di: Bouajjani, Ahmed, et al.
Pubblicazione: (2025)

Likelihood hacking in probabilistic program synthesis
di: Karwowski, Jacek, et al.
Pubblicazione: (2026)

Transformers for Program Termination
di: Alon, Yoav, et al.
Pubblicazione: (2026)

Synthetic Programming Elicitation for Text-to-Code in Very Low-Resource Programming and Formal Languages
di: Mora, Federico, et al.
Pubblicazione: (2024)

Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs
di: Cassano, Federico, et al.
Pubblicazione: (2023)

A Comprehensive Guide to Combining R and Python code for Data Science, Machine Learning and Reinforcement Learning
di: Navarro, Alejandro L. García, et al.
Pubblicazione: (2024)

PolyBlocks: A Compiler Infrastructure for AI Chips and Programming Frameworks
di: Bondhugula, Uday, et al.
Pubblicazione: (2026)

Learning Minimal Neural Specifications
di: Geng, Chuqin, et al.
Pubblicazione: (2024)

Enhancing the Code Reasoning Capabilities of LLMs via Consistency-based Reinforcement Learning
di: Qin, Zhanyue, et al.
Pubblicazione: (2026)

ExeDec: Execution Decomposition for Compositional Generalization in Neural Program Synthesis
di: Shi, Kensen, et al.
Pubblicazione: (2023)

Uncertainty Quantification for Neurosymbolic Programs via Compositional Conformal Prediction
di: Ramalingam, Ramya, et al.
Pubblicazione: (2024)

Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support
di: Reichelt, Tim, et al.
Pubblicazione: (2023)