arXiv AI
AutoOR: Scalably Post-training LLMs to Autoformulate Operations Research Problems
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arXiv AI
Aug 20
AutoOR: Scalably Post-training LLMs to Autoformalize Operations Research Problems
AutoOR is a scalable synthetic data generation and reinforcement learning pipeline that trains large language models to autoformalize operations research problems expressed in natural language across linear, mixed‑integer, and non‑linear categories. By generating verified training data from standard optimization forms and using solver execution feedback as a reward signal, AutoOR enables post‑training of an 8B model to achieve state‑of‑the‑art or competitive results on six established OR benchmarks, matching significantly larger frontier models. For non‑linear problems involving physical dynamics, a curriculum RL strategy bootstraps from limited initial data, making this class tractable for post‑training.
arXiv AI
Aug 18
ML-AutoResearch: Training Machine Learning Research Agents with Automatically Generated Environments
arXiv:2603. 17216v2 Announce Type: replace Abstract: With the advent of AI agents, automated scientific discovery is becoming an increasingly plausible goal.
Hugging Face Trending Papers
Jun 3
AutoLab: Can Frontier Models Solve Long-Horizon Auto Research and Engineering Tasks?
Scientific and engineering progress is fundamentally a long-horizon iterative process: proposing changes, running experiments, measuring outcomes, and continuously refining artifacts. Yet existing benchmarks for frontier models primarily evaluate either single-turn responses or short-horizon agent trajectories, failing to capture the challenges of sustained iterative improvement over extended time horizons.
arXiv AI
Sep 12
Beyond Verified Answers: Solver-Informed Self-Distillation for Bootstrapping Operations Research Language Models
The paper introduces SOLID, a framework that enables operations research language models to self-improve without relying on verified answers or external evaluators. SOLID uses solver-generated artifacts from the model’s own rollouts to create pseudo-references, clustering objectives and applying group-relative advantages for dense self-supervision. Experiments on multiple OR benchmarks show that SOLID enhances solution accuracy for both general-purpose and OR-tuned models compared to outcome-only training.
arXiv AI
Jun 4
AutoLab: Can Frontier Models Solve Long-Horizon Auto Research and Engineering Tasks?
arXiv:2606. 05080v1 Announce Type: new Abstract: Scientific and engineering progress is fundamentally a long-horizon iterative process: proposing changes, running experiments, measuring outcomes, and continuously refining artifacts.
arXiv Computation and Language
2d ago
SemOPT: Fixing Semantic Errors in LLM-based Optimization Modeling via Reward-Guided Search
arXiv:2609.37361v1 Announce Type: new Abstract: Operations research supports decision-making in domains such as energy, economics, and healthcare. Solving operations research problems typically begin...