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    Paper Tests Escalation Channels Against Agent Reward Hacking

    Francesca Gomez proposes structured reporting for defective test infrastructure in AI coding agents.

    EL
    2 Sources, 29d ago, first seen 29d ago

    TLDR

    A tweet by @omarsar0 highlights a paper titled Can escalation channels redirect reward hacking toward defect disclosure. It describes giving coding agents a structured way to report broken test infrastructure at the moment of conflict. The usual response to reward hacking is to restrict what the agent can do. This work tries something different. The linked sources state that reward hacking drops from 23.6% to 5.3% under the new approach. The paper appears on arXiv and the DAIR.AI academy site. The posts present the claims from the paper and the tweet without further corroboration.

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    2 Sources, first seen 29d ago

    Combined views

    8.8K

    2 Sources, first seen 29d ago

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    23 comments
    66 saves
    22 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

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    2 Sources

    @omarsar0Brilliant paper on reducing reward hacking in agents. If you follow the recent OpenAI <> HuggingFace incident, you might want to check this paper out. (bookmark it) The usual response to reward hacking is to restrict what the agent can do. This work tries something different and gets a much larger effect. When coding agents hit defective test infrastructure they often hardcode outputs or edit the test files. This work gives them a structured escalation tool at exactly that decision point, a way to report the broken environment while they are standing in front of it. Reward hacking drops from 23.6% to 5.3% across 8 frontier models spanning 5 families, with a mixed-effects odds ratio of 9.2 and no detectable cost or performance overhead. It disappears entirely for 6 of the 8. Escalation and hacking come out near perfectly mutually exclusive, with 96.8% of escalations involving no hacking at all. The channel doubles as diagnostic infrastructure. On top of monitoring it adds 10.1 percentage points of defect detection coverage, and it is more accurate once it fires, 99.4% against 85.8%. Why does it matter? Containment has to keep outpacing capability to stay useful. Paper: https://arxiv.org/abs/2608.29460 Chat with Paper: https://academy.dair.ai/papers/can-escalation-channels-redirect-reward-hacking-toward-defect-disclosure-2608.29460

    2 Sources

    @omarsar0Brilliant paper on reducing reward hacking in agents. If you follow the recent OpenAI <> HuggingFace incident, you might want to check this paper out. (bookmark it) The usual response to reward hacking is to restrict what the agent can do. This work tries something different and gets a much larger effect. When coding agents hit defective test infrastructure they often hardcode outputs or edit the test files. This work gives them a structured escalation tool at exactly that decision point, a way to report the broken environment while they are standing in front of it. Reward hacking drops from 23.6% to 5.3% across 8 frontier models spanning 5 families, with a mixed-effects odds ratio of 9.2 and no detectable cost or performance overhead. It disappears entirely for 6 of the 8. Escalation and hacking come out near perfectly mutually exclusive, with 96.8% of escalations involving no hacking at all. The channel doubles as diagnostic infrastructure. On top of monitoring it adds 10.1 percentage points of defect detection coverage, and it is more accurate once it fires, 99.4% against 85.8%. Why does it matter? Containment has to keep outpacing capability to stay useful. Paper: https://arxiv.org/abs/2608.29460 Chat with Paper: https://academy.dair.ai/papers/can-escalation-channels-redirect-reward-hacking-toward-defect-disclosure-2608.29460