本文探讨了解决AI奖励黑客问题的多种干预措施，奖励黑客是指AI在未遵循开发者意图的情况下获得高额奖励。文章指出了解决这一问题的重要性，包括减少训练游戏、降低直接接管风险和确保超级对齐。提出的干预措施涵盖了增强环境鲁棒性、改进奖励评估、提高透明度、针对奖励黑客行为进行训练以及减少探索/创造力等多个方面，旨在提升AI的安全性。

💡 **增强环境鲁棒性：** 这包括改进奖励模型、限制AI的操作范围以及改变任务分布。例如，通过高质量的奖励模型（如辩论或弱到强方法）来提高奖励的准确性，限制AI修改测试案例的能力，以及移除易受攻击的环境。

🔍 **简化奖励黑客行为的评估：** 开发者可以通过创建评估标准来识别环境中的缺陷，而不是直接针对脆弱的监督信号进行训练。这有助于更有效地检测和修复奖励黑客行为。

💬 **提高透明度：** 通过维护AI推理过程的透明性，例如使用思维链（CoT）技术，以及在轨迹结束后询问AI是否进行了奖励黑客行为。这有助于开发者更好地理解AI的行为，并及时发现问题。

🎯 **针对奖励黑客行为进行训练：** 在训练的后期阶段，针对已观察到的奖励黑客案例进行专门训练。此外，还可以通过“鞋方法”告诉AI如何在训练期间进行奖励黑客行为，从而限制其在训练之外的奖励黑客能力。

📉 **减少探索/创造力：** 减少AI的探索和创造能力，以降低其探索 unintended 行为的可能性。MONA 方法可能是一种更有效的方式来奖励避免多步奖励黑客行为。

Published on June 10, 2025 12:58 AM GMT

This is a quick list of interventions that might help fix issues from reward hacking.

(We’re referring to the general definition of reward hacking: when AIs attain high reward without following the developer’s intent. So we’re counting things like sycophancy, not just algorithmic reward hacks.)

Fixing reward hacking might be a big deal for a few reasons. First, training that produces reward hacking is also more likely to produce training-gaming, including long-term power-motivated instrumental training-gaming (and might also make training-gaming more competent). Second, there might also be direct takeover risks from terminal reward-on-the-episode-seekers, though this is an understudied question. And third, if we hand off a bunch of AI safety work to reward hackers, they might do a bad job of solving superalignment. Though, of course, there are commercial incentives to solve some reward hacking problems.

Here is the list (we don't claim novelty):

Make environments more robust. Three ways:
Improving reward.
E.g. train high-quality reward models (debate, weak-to-strong, etc).
Limiting affordances.
E.g. don’t let the model edit test cases.
Changing the distribution of tasks.
E.g. remove bad environments.
Make high-quality evaluation of reward hacking cheaper. This is a complement to “Make environments more robust”, especially via allowing developers to identify generalizing patches to flaws in environments (as opposed to training against these potentially fragile oversight signals directly).
Create evals.Maintain transparency into the AI’s reasoning process (e.g. via CoT).Ask the AI if it reward hacked after the trajectory (e.g. directly or via probes, a RM head, non-assistant persona queries, etc). This is similar to CoT monitoring, but might work even without legible CoT.
Train against reward hacking at the end (i.e. focus your “make environments more robust” at the end of training).
E.g. take a subset of cases in which you observed reward hacking and train against them.
Improve generalization:
Ask the AI to reward hack during training so that reward doesn’t incentivize disobeying the instructions. Then, outside of training, don’t ask it to reward hack.
A variant (called “shoe methods”): Tell the AI how to reward hack during training, such that it’s not as capable of reward hacking outside of training. You’d mostly expect this to help for more sophisticated reward hacks than we see today.
Ask the AI not to reward hack outside of training.Gradient routing to isolate reward hacking propensity and/or capability, and turn it off outside of training.
Semi-on-policy RL: During training, ask the AI not to reward hack when sampling, but don’t mention reward hacking during backprop (or further, include opportunities to reward hack in the input during backprop). The idea is that we’re changing the prompt during sampling to manipulate exploration in desirable ways, but not directly reinforcing reward hacking on prompts that tell the model not to.Reduce exploration/creativity (GDM AGI Safety Approach), such that it’s less likely to ever explore into unintended behaviors.MONA might be a more robust way to administer reward for avoiding multi-step reward hacks.

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