Maximizing joint conditional mutual information I(Y; C_Z, W, Z | X) decomposes multi-objective LLM alignment into preference-specific DPO terms plus an I(Y;W|X) exploration term that reduces reward-distribution overlap.
In Advances in Neural Information Processing Systems (NeurIPS), volume 38
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Hybrid-DPO, which trains LLMs on preference pairs scored by a DeBERTa NLI entailment signal plus a verifier fluency score, improves NLI entailment over SFT in 11 of 15 model-domain cells and documents a persistent verbosity bias in GPT-4o-mini judging.
MedSynapse-V proposes a latent memory evolution framework with meta-query prior retrieval, causal counterfactual refinement via RL, and intrinsic memory transition to improve diagnostic accuracy over chain-of-thought baselines in medical VLMs.
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Multi-Objective Exploration and Preference Optimization via Mutual Information
Maximizing joint conditional mutual information I(Y; C_Z, W, Z | X) decomposes multi-objective LLM alignment into preference-specific DPO terms plus an I(Y;W|X) exploration term that reduces reward-distribution overlap.
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RLearner-LLM: Balancing Logical Grounding and Fluency in Large Language Models via Hybrid Direct Preference Optimization
Hybrid-DPO, which trains LLMs on preference pairs scored by a DeBERTa NLI entailment signal plus a verifier fluency score, improves NLI entailment over SFT in 11 of 15 model-domain cells and documents a persistent verbosity bias in GPT-4o-mini judging.
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MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution
MedSynapse-V proposes a latent memory evolution framework with meta-query prior retrieval, causal counterfactual refinement via RL, and intrinsic memory transition to improve diagnostic accuracy over chain-of-thought baselines in medical VLMs.