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Instead of directly aligning to a flawed pseudo-source domain in test-time adaptation, a semantic bridge approach significantly boosts performance by first rectifying the pseudo-source using universal semantics.
By modeling the distribution of confidence scores, DistriVoting significantly boosts the accuracy of large reasoning models, outperforming existing confidence-based selection methods across diverse benchmarks.
Forget difficulty-based heuristics: InSight leverages weighted mutual information to select RL training data, boosting LLM reasoning and alignment with up to 2.2x speedup.
