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Reward models are central to preference alignment for legal tasks, yet existing reward model benchmarks are still primarily centered on general-domain preferences. In this work, we introduce LegalRewardBench, the first human-verified benchmark for evaluating reward models in the legal domain. The benchmark organizes le...
Experiments show that MotiveMob consistently achieves better distributional fidelity than competitive pretraining-based and prompting-based methods under user-level and temporal distribution shifts, demonstrating robust generalization to out-of-distribution mobility patterns.
This paper considers multilingual pretraining as a cooperative game in which each language acts as a player that jointly contributes to pretraining, gaining the resulting reduction in test loss as the payoff and proposes a game-theoretic multilingual scaling law called ShapleyLaw.
It is shown that a lightweight LLM-only DPO update on tiny single-object-pair synthetic data mitigates the bias, lifting four-way robust accuracy by up to 100 points on synthetic data, and by 68.1 points on broader evaluation datasets WhatsUp, SpatialMQA-Direct, and VSR.
A novel Multi-Margin Collaborative Debiasing (MMCD) framework is proposed, which adaptively integrates frequency-aware, confidence-aware, and difficulty-aware angular margins with a dynamic, difficulty-aware contrastive learning mechanism to reshape decision boundaries under biased training conditions.
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