Anchor-guided variance-aware reward modeling uses two response-level anchors to resolve non-identifiability in Gaussian models of pluralistic preferences, yielding provable identification, a joint training objective, and improved RLHF performance.
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Softmax Transformers with specific parameters implement iterative weighted softmax TD learning for in-context policy evaluation, with evaluation error decaying over layers and those parameters globally minimizing pretraining loss.
A frozen average of the last two cycles matches or exceeds eight shape-learning alternatives on 97 GIFT-Eval configurations for periodic time series forecasting.
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Variance-aware Reward Modeling with Anchor Guidance
Anchor-guided variance-aware reward modeling uses two response-level anchors to resolve non-identifiability in Gaussian models of pluralistic preferences, yielding provable identification, a joint training objective, and improved RLHF performance.
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Beyond Linear Attention: Softmax Transformers Implement In-Context Reinforcement Learning
Softmax Transformers with specific parameters implement iterative weighted softmax TD learning for in-context policy evaluation, with evaluation error decaying over layers and those parameters globally minimizing pretraining loss.
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Don't Learn the Shape: Forecasting Periodic Time Series by Rank-1 Decomposition
A frozen average of the last two cycles matches or exceeds eight shape-learning alternatives on 97 GIFT-Eval configurations for periodic time series forecasting.