# The Verification Horizon: No Silver Bullet for Coding Agent Rewards

Page: https://stenobird.com/podcast/daily-paper-cast-7079649/the-verification-horizon-no-silver-bullet-for-coding-agent-rewards
Text version: https://stenobird.com/podcast/daily-paper-cast-7079649/the-verification-horizon-no-silver-bullet-for-coding-agent-rewards.md
Podcast: [Daily Paper Cast](https://stenobird.com/podcast/daily-paper-cast-7079649)
Published: 2026-06-28T05:41:33+00:00
Episode link: https://share.transistor.fm/s/81b0f9d9
Audio file: https://media.transistor.fm/81b0f9d9/b2d7265a.mp3
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JSON: https://stenobird.com/v1/public/podcasts/daily-paper-cast-7079649/episodes/the-verification-horizon-no-silver-bullet-for-coding-agent-rewards
Duration seconds: 1375

## Resource

🤗 Upvotes: 38 | cs.AI, cs.CL Authors: Binghai Wang, Chenlong Zhang, Dayiheng Liu, Jiajun Zhang, Jiawei Chen, Mouxiang Chen, Rongyao Fang, Siyuan Zhang, Xuwu Wang, Yuheng Jing, Zeyao Ma, Zeyu Cui Title: The Verification Horizon: No Silver Bullet for Coding Agent Rewards Arxiv: http://arxiv.org/abs/2606.26300v1 Abstract: A classical intuition holds that verifying a solution is easier than producing one. For today's coding agents, this intuition is being inverted: as foundation models develop stronger reasoning capabilities and engineering harnesses grow more sophisticated, generating complex candidate solutions is no longer difficult -- reliably verifying them has become the harder problem. Every verifier we can build is only a proxy for human intent, never the intent itself. This makes verification subject to a twofold difficulty: first, intent is underspecified by nature, making it inherently hard to faithfully check whether it has been fulfilled; second, during model training, optimization widens the gap between proxy and intent -- manifesting as reward hacking or signal saturation. To address this, we characterize the quality of verification signals along three dimensions -- scalability, faithfulness, and robustness -- and argue that achieving all three simultaneously is the central challenge. We further study four reward constructions: a test verifier for general coding tasks, a rubric verifier for frontend tasks, the user as verifier for real-world agent tasks, and an automated agent verifier for long-horizon tasks. Across different task types and policy capability levels, we conduct in-depth analysis and experiments on the core challenges of reward design and how to more effectively leverage reward signals. Experiments show that targeted verification design can ef…

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