Autonomous programming agents frequently fail to follow instructed plans, falling back on incomplete internalized workflows, while standard plans and periodic reminders improve performance but poor plans can degrade it more than no plan.
Mona: Myopic optimization with non-myopic approval can mitigate multi-step reward hacking
2 Pith papers cite this work. Polarity classification is still indexing.
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The paper introduces the Proxy Compression Hypothesis as a unifying framework explaining reward hacking in RLHF as an emergent result of compressing high-dimensional human objectives into proxy reward signals under optimization pressure.
citing papers explorer
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Evaluating Plan Compliance in Autonomous Programming Agents
Autonomous programming agents frequently fail to follow instructed plans, falling back on incomplete internalized workflows, while standard plans and periodic reminders improve performance but poor plans can degrade it more than no plan.
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Reward Hacking in the Era of Large Models: Mechanisms, Emergent Misalignment, Challenges
The paper introduces the Proxy Compression Hypothesis as a unifying framework explaining reward hacking in RLHF as an emergent result of compressing high-dimensional human objectives into proxy reward signals under optimization pressure.