arXiv (All)
open access
CalibForge: Adversarial Solver Calibration for Scaling Learnable Terminal Tasks
Training terminal agents requires executable and verifiable tasks that are not merely solvable, but appropriately challenging for learning. Executable validation establishes feasibility, yet does not reveal how a task behaves relative to a given solver setting. In this paper, we present CalibForge,
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- CalibForge: Adversarial Solver Calibration for Scaling Learnable Terminal Tasks#432510
- Multi-Objective Reinforcement Learning Using Routed Ensembles and Trajectory Attribution#1459256
- Multi-Objective Reinforcement Learning Using Routed Ensembles and Trajectory Attribution#1450784
- VeriHarness: Scaling Agentic Verification for Long-Horizon Tasks#1155907
- CUA-Gym: Scaling Verifiable Training Environments and Tasks for Computer-Use Agents#228184
Record · ID 1488442
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive