arXiv Machine Learning
Harness Scaling for Generalizable Agentic Reinforcement Learning
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
Aug 18
ClawGym II: Exploring Black-Box RL on Agent Harness
arXiv:2608. 16798v1 Announce Type: cross Abstract: Agent harnesses have substantially improved performance on long-horizon tasks by coordinating agent interactions with the environment.
arXiv Machine Learning
Aug 28
Learning Generalizable Behaviors for Terminal Agents
The paper introduces the Agentic Compositional Generalization hypothesis, suggesting that reinforcement learning (RL) primarily refines high‑level decision‑making behaviors that orchestrate pre‑trained low‑level skills, rather than teaching new domain‑specific skills from scratch. It proposes River, a training recipe that enhances reward quality by filtering low‑quality synthetic environments and adding process‑level behavior regularization. Using River, RL‑trained agents outperform other open‑source 8B models on four terminal‑agent benchmarks, achieving significant gains with fewer than 30% of the training environments.
arXiv AI
Sep 4
Out-of-Distribution Generalisation with Sequence Models in Offline Multi-Agent Reinforcement Learning
The paper investigates zero‑shot task generalisation in offline multi‑agent reinforcement learning by extending sequence‑modeling architectures to support multi‑task observation and action spaces and variable agent counts. It finds that increasing task diversity, rather than merely enlarging the dataset, is the key driver for robust zero‑shot transfer. Experiments on four challenging environments show a 3.2× mean improvement on held‑out tasks compared to single‑task models and outperform strong behaviour‑cloning baselines.
arXiv Computation and Language
Oct 9
MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvement
MiMo‑V2.6 is an omni‑modal reinforcement learning framework that scales compute to advance large foundation models toward self‑improvement. It trains on a broad multimodal corpus before RL, then scales RL along three axes: larger batches and throughput, diverse environments across code, general, visual, and cyber domains, and enhanced grader compute for better reward signals. The system incorporates infrastructure such as a frozen MoE router, multi‑layer defense against reward hacking, unified trajectory representation, high‑concurrency rollout, and training‑inference consistency, and it is open‑source to support reproducibility and further research.
arXiv Machine Learning
Jun 5
Training One Model to Master Cross-Level Agentic Actions via Reinforcement Learning
arXiv:2512. 09706v2 Announce Type: replace Abstract: The paradigm of agentic AI is shifting from engineered complex workflows to post-training native models.
arXiv Machine Learning
1d ago
How Data Scales in Agentic Reinforcement Learning: Laws and Synthesis Strategies
The paper investigates how data should be scaled in agentic reinforcement learning, where data is now a structured design space of tasks, environments, and trajectories. By fitting per‑axis scaling laws across various large language models and RL algorithms, the authors find that environment diversity has the strongest impact, followed by task and trajectory diversity. Using these laws, they price synthesis methods, identify critical synthetic‑to‑real ratios, and formulate a compute‑optimal budget allocation that can improve efficiency by up to 1.7× compared to the common practice of scaling trajectories alone.