arXiv AI
4d ago

Checking the Checkers: A Synthesis of Verifier Noise and Robustness in RLVR

The paper surveys 58 studies on verifier errors in reinforcement learning with verifiable rewards (RLVR), examining how such errors affect training and which mitigations are effective. It finds that the type of error—rather than its frequency—has a larger impact on learning dynamics, with random errors mainly slowing progress and false positives potentially causing learning plateaus or collapse. The authors compile findings into comparison tables, reconcile conflicting reports, and propose a reporting protocol to standardize future research.

By Savyasachi Gupta, Aniruddha Srinivas Joshi
arXiv AI
Oct 2

Verify Claims, Not Scores: Evidence-Based Verification of Modular Agents

The paper proposes a claim‑specific verification audit for modular agents that replaces aggregate task scores with evidence‑based evaluations. Each agent conclusion is recorded with supporting evidence and classified as supported, unsupported, unresolved, or not evaluated, along with the boundary of validity. The audit employs three tools—oracle policies, perfect component replacements, and verifier‑score tests—to trace value changes, locate lost value, and assess verifier effectiveness, demonstrated on a portfolio‑allocation agent in a synthetic market.

By Ali Atiah Alzahrani
arXiv AI
Jun 30

SEVA: Self-Evolving Verification Agent with Process Reward for Fact Attribution

arXiv:2606. 29713v1 Announce Type: cross Abstract: Hallucination is the reliability bottleneck for LLM-based agents, and fact attribution verifiers are the last line of defense -- yet today's verifiers emit only opaque binary labels, leaving agents unable to self-correct and operators unable to audit.

By Aojie Yuan, Yi Nian, Haiyue Zhang, Zijian Su, Yue Zhao