Consistency Is Not Validity: Self-Rewarded Moral Argumentation Makes Language Models Decisive but Pliable
Marius De Kuthy Meurers, Aaron Parisi and Bernd Bohnet (Google DeepMind) train a policy by self-judged argument tournaments on moral dilemmas and find it becomes more consistent without becoming more valid.
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Data. Authentic dilemmas extracted from classic novels, each verified by a logic solver to contain a real conflict.
Training. RL in a self-judged tournament with masked thinking, where the policy rates pairs of its own arguments on quality; the judge agrees with human annotators.
Result. On MoralChoice, ELEPHANT and MORABLES the policy becomes more decisive and invariant to option order, but sycophancy and reframing still change its answers.
Conclusion. Order invariance can be optimized without improving the moral judgment it is supposed to indicate.
Abstract
LLMs are increasingly consulted for moral judgment. We aim to improve performance on such tasks by rewarding good arguments, on the premise that arguing well increases the likelihood of judging well. We train a reinforcement learning policy initialized from a state-of-the-art language model in a self-judged tournament with masked thinking: the generated arguments are judged pairwise on argument quality by the policy itself. As an empirical basis, we extract authentic dilemmas from classic novels, using a logic solver to verify that each constitutes a conflict. We show that our judge rates argument quality like human annotators, and the policy then adopts the language register that it rewards. Testing on the external benchmarks MoralChoice, ELEPHANT, and MORABLES, we find that the policy becomes more decisive and invariant to the order in which options are presented, but sycophancy and reframing remain a challenge to validity. Two ablation studies with synthetic dilemmas and unmasked thinking match the reordering gains, but show a weaker register shift and higher sycophancy. We conclude that consistency is not validity: consistency under reordering can be optimized without improving the moral judgment it is meant to signal.