aiqbench analysis
The paper studies goal misgeneralization in reinforcement learning agents, where a policy performs competently out of distribution but pursues the wrong objective. It identifies three distinct failure types (target substitution, action shortcuts, and cue competition) and reports that the right intervention depends on how deep in the network the goal is lost. For alignment researchers, the practical point is that a single fix for goal misgeneralization is unlikely to work, and interventions need to match the specific failure. The results come from Procgen and robot benchmarks, so their transfer to larger systems remains untested.
What Happens to the Goal? Distinct Failures of Policy Alignment in Goal Misgeneralization
arXiv:2610.19731v1 Announce Type: new Abstract: Goal misgeneralization is a common failure mode in which an agent behaves competently out of distribution but does not pursue the intended goal. However, we still have a weak understanding of what happens to the intended goal in misgeneralizing agents. Across three Procgen environments and five robot tasks, we find three distinct forms of policy-goal misalignment: (1) functional target substitution, where behavior follows an alternative goal, (2) a policy shortcut, where behavior follows a learned action strategy, and (3) cue competition between intended and proxy goal cues. We distinguish these regimes by tracing where goal information remains available internally, how strongly it influences the policy, and what role it plays in action selection, and find that the goal is lost at different depths. Where the goal reaches the decision, as in cue competition, steering redirects the policy to the goal's location, raising intended-goal reach from 16.0% to 95.3%. Where it reaches the policy but not the decision, only new downstream wiring recovers goal reach, from 31.6% to 84.3% in the environment with the action shortcut. Where it is inert, neither works. Our results show that goal misgeneralization is not a single failure of goal representation, but distinct failures of policy alignment at different depths, tightly connected to which recovery works.
Title:What Happens to the Goal? Distinct Failures of Policy Alignment in Goal Misgeneralization
Abstract:Goal misgeneralization is a common failure mode in which an agent behaves competently out of distribution but does not pursue the intended goal. However, we still have a weak understanding of what happens to the intended goal in misgeneralizing agents. Across three Procgen environments and five robot tasks, we find three distinct forms of policy-goal misalignment: (1) functional target substitution, where behavior follows an alternative goal, (2) a policy shortcut, where behavior follows a learned action strategy, and (3) cue competition between intended and proxy goal cues. We distinguish these regimes by tracing where goal information remains available internally, how strongly it influences the policy, and what role it plays in action selection, and find that the goal is lost at different depths. Where the goal reaches the decision, as in cue competition, steering redirects the policy to the goal's location, raising intended-goal reach from 16.0% to 95.3%. Where it reaches the policy but not the decision, only new downstream wiring recovers goal reach, from 31.6% to 84.3% in the environment with the action shortcut. Where it is inert, neither works. Our results show that goal misgeneralization is not a single failure of goal representation, but distinct failures of policy alignment at different depths, tightly connected to which recovery works.
10 pages main text, 120 pages total. Code: this https URL
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Robotics (cs.RO)
arXiv:2610.19731 [cs.LG]
(or arXiv:2610.19731v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.19731 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 333 words
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