elvis@omarsar0 · XQwen 发布关于强化学习编码智能体的新工作，指出 LLM 的奖励黑客问题。他们系统研究了编码智能体中的各种奖励信号——测试通过率、LLM 评判器和执行轨迹，发现每种信号都存在一个“地平线”：超出该界限后，信号不再跟踪真实正确性，而是被奖励黑客利用。论文认为长周期编码的奖励设计本质上是地平线问题，指标的选择不如它能持续跟踪正确性的时长重要。
Qwen publishes new work on RL coding agents.
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The idea is to continually build a verification system that co-evolves with AI agents.
LLMs suffer from all sorts of reward hacking issues. This work studies coding-agent reward signals， test pass rates， LLM judges， and execution traces， and shows each one has a horizon beyond which it stops tracking real correctness and starts getting hacked.
They report that reward design for long-horizon coding is really a horizon problem. The metric you pick matters less than how long it keeps tracking correctness， and the paper finds where each signal crosses that line.
Paper： https://arxiv.org/abs/2606.26300
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