arXiv Machine Learning July 31, 2026 By Mohand Mezmaz, Gr\'egoire Danoy

LM-GRASP: Instance-Specific Language Models for Combinatorial Construction via Online Imitation Learning

Read the original on arXiv Machine Learning →

arXiv:2607. 28135v1 Announce Type: new Abstract: Machine learning for combinatorial optimization typically relies on neural constructors trained via reinforcement learning on large offline datasets for a fixed problem class-incurring high pretraining costs and generalizing poorly outside the training distribution.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 23

In-the-Flow Agentic System Optimization for Effective Planning and Tool Use

arXiv:2510. 05592v2 Announce Type: replace Abstract: Outcome-driven reinforcement learning has advanced reasoning in large language models (LLMs), but prevailing tool-augmented approaches train a single, monolithic policy that interleaves thoughts and tool calls under full context; this scales poorly with long horizons and diverse tools and generalizes weakly to new scenarios.

By Zhuofeng Li, Haoxiang Zhang, Seungju Han, Sheng Liu, Jianwen Xie, Yu Zhang, Yejin Choi, James Zou, Pan Lu

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Jun 30

Evolution Fine-Tuning: Learning to Discover Across 371 Optimization Tasks

arXiv:2606. 29082v1 Announce Type: cross Abstract: Would experience designing faster GPU kernels also help close in on a long-standing open mathematical conjecture?

By Young-Jun Lee, Seungone Kim, Minki Kang, Alistair Cheong Liang Chuen, Zerui Chen, Seungho Han, Taehee Jung, Dongyeop Kang

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Aug 3

When Does On-Policy Interaction Help? Representational Tradeoffs in Value-Based Imitation Learning

arXiv:2607. 29617v1 Announce Type: cross Abstract: Imitation learning (IL)---training an agent to replicate expert behavior from demonstrations---underpins applications from robotics to language model training.

By Luca Viano, Antoine Moulin, Audrey Huang, Volkan Cevher, Philip Amortila, Dylan J. Foster

llms agents robotics efficiency

arXiv Machine Learning

Jul 21

Planning with Transformers: Chain of Computation and Structured Context Windows

arXiv:2607. 17710v1 Announce Type: new Abstract: Large Language Models (LLMs) have had a remarkable impact across many areas of machine learning.

By Ehsan Futuhi, Nathan R. Sturtevant

llms

arXiv Machine Learning

Jul 3

DecompRL: Solving Harder Problems by Learning Modular Code Generation

arXiv:2607. 02390v1 Announce Type: new Abstract: How can Large Language Models (LLMs) solve problems they currently cannot?

By Juliette Decugis, Fabian Gloeckle, Francis Bach, Taco Cohen, Gabriel Synnaeve

llms reinforcement-learning

arXiv Machine Learning

Jun 29

Support-Constrained RL Enables Real-World Policy Improvement without Real-World Experience

arXiv:2606. 27475v1 Announce Type: cross Abstract: Robots trained on real world data tend to be imprecise, slow, and brittle to perturbations.

By Raymond Yu, William Huey, Mustafa Mukadam, Anusha Nagabandi, Abhishek Gupta

reinforcement-learning robotics efficiency

Related stories

In-the-Flow Agentic System Optimization for Effective Planning and Tool Use

Evolution Fine-Tuning: Learning to Discover Across 371 Optimization Tasks

When Does On-Policy Interaction Help? Representational Tradeoffs in Value-Based Imitation Learning

Planning with Transformers: Chain of Computation and Structured Context Windows

DecompRL: Solving Harder Problems by Learning Modular Code Generation

Support-Constrained RL Enables Real-World Policy Improvement without Real-World Experience