arXiv Machine Learning
Harness Policy Distillation: Aligning Agent Policies with Runtime Semantics
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
Jun 12
HarnessBridge: Learnable Bidirectional Controller for LLM Agent Harness
arXiv:2606. 12882v1 Announce Type: new Abstract: Large language models are increasingly deployed as agents for long-horizon tasks, yet their performance is shaped not only by model capability and environment design, but also by the harness that mediates agent--environment interaction.
arXiv Machine Learning
Aug 7
EvoHarness-RL: Learning Self-Evolving Runtime Harness for Long-Horizon LLM Agents
arXiv:2608. 05446v1 Announce Type: new Abstract: Long-horizon LLM agents increasingly rely on external execution support to maintain state, track progress, invoke tools, verify outcomes, and reuse experience across interactions.
arXiv Computation and Language
Oct 8
CoTrace: Data Recipes for Training Terminal Agents with Harness-Model Co-Evolution
arXiv:2610.10426v1 Announce Type: new Abstract: Terminal-agent capability depends jointly on model weights and the runtime harness that formats prompts, binds tools, and handles error recovery. Exist...
arXiv AI
Sep 1
SUN: Persistent Programs For Language-Grounded Control-to-Learning-to-Real Policies
arXiv:2608.31167v1 Announce Type: cross Abstract: Bridging model-based control and learned policies in long-horizon manipulation has harbored a silent disagreement: control executes specified objecti...
Hugging Face Trending Papers
Oct 7
CoTrace: Data Recipes for Training Terminal Agents with Harness-Model Co-Evolution
Terminal-agent capability depends jointly on model weights and the runtime harness that formats prompts, binds tools, and handles error recovery. Existing harness-model co-evolution approaches improve...
arXiv AI
Sep 2
EmbodiedSkills: A Unified Framework for Orchestrating, Training, and Deploying VLA Agents
EmbodiedSkills is a unified framework that treats each skill decision as an execution proposal, checking prerequisites and verifying outcomes during long‑horizon vision‑language‑action tasks. It connects high‑level skill selection, bounded low‑level VLA execution, and post‑action verification through a fixed executable‑skill interface, enabling easy replacement of low‑level policies and recording of structured trajectories for supervision and adaptation. Instantiated with Qwen3‑VL and OpenPI/pi0.5 on RoboTwin 2.0 and LIBERO, the framework achieves high success rates (86.20% and 97.40% respectively) and demonstrates effective memory‑dependent task performance.