arXiv Machine Learning
Jul 16

Where Should RL Post-Training Compute Go? Model Size, Search, Learning, and Feedback

arXiv:2607. 13389v1 Announce Type: new Abstract: Reinforcement Learning (RL) post-training is increasingly used to adapt foundation models for reasoning, planning, and feedback-driven robot-learning pipelines, but constrained post-training resources are often summarized by a single total FLOP budget.

By Patrick Wilhelm, Odej Kao
arXiv Machine Learning
Sep 25

Automatic Harness Evolution for Hardware Design Verification: Can LLMs Consolidate Gains Across Discovered Harnesses?

The study investigates whether large language models (LLMs) can automatically improve the harnesses used for hardware design verification. By evolving harnesses around a fixed subject model on 12 proprietary root‑cause localization tasks, the researchers found that automatically evolved harnesses increased completed attempts by 71‑76% and task coverage by 80‑100%, though overall correct attempts improved only 18‑24%. Despite these gains, the evolved harnesses did not consistently consolidate into a single dominant solution across tasks and metrics, and a separate cross‑benchmark case showed that a repair harness could yield a 35.6% increase in functional passes over a baseline.

By Kidus Seyoum, Ajay Mittur
arXiv AI
Jun 19

Connect the Dots: Training LLMs for Long-Lifecycle Agents with Cross-Domain Generalization Via Reinforcement Learning

arXiv:2606. 20002v1 Announce Type: cross Abstract: This work presents a general framework for training large language models (LLMs) to "Connect the Dots" (CoD), a meta-capability required by long-lifecycle agents: as an LLM-based AI agent gets deployed in an environment, it solves a long sequence of tasks while continuously exploring the environment, learning from its own experiences, and iteratively self-updating its context about the environment, thereby achieving progressively better performance on future tasks conditioned on the updated context.

By Yanxi Chen, Weijie Shi, Yuexiang Xie, Boyi Hu, Yaliang Li, Bolin Ding, Jingren Zhou