Learnable Chernoff Baselines for Inference-Time Alignment
#Learnable Chernoff Baselines #LCBs #Inference‑time alignment #Reward‑guided alignment #KL‑regularized #Exponentially tilted kernels #Black‑box sampling #ArXiv
📌 Key Takeaways
- LCBs enable efficient sampling from exponential tilting kernels arising in KL‑regularized reward alignment.
- The method uses only black‑box sampling access to pretrained models, avoiding costly inference steps.
- It offers an approximate but scalable alternative to existing architecture‑specific solutions.
- The approach is presented as a replacement‑cross annotation update on arXiv.
- It targets generative models requiring fast, reward‑guided inference.
📖 Full Retelling
The authors of the 2026 arXiv preprint arXiv:2602.07738v2 introduce Learnable Chernoff Baselines (LCBs) for efficient inference‑time reward‑guided alignment in generative models, addressing the limitations of existing methods that rely on architecture‑specific adaptations or computationally costly inference procedures.
🏷️ Themes
Generative modeling, Inference‑time alignment, Reward‑guided training, KL regularization, Sampling efficiency
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Original Source
arXiv:2602.07738v2 Announce Type: replace-cross
Abstract: We study inference-time reward-guided alignment for generative models. Existing methods often rely on either architecture-specific adaptations or computationally costly inference procedures. We introduce Learnable Chernoff Baselines (LCBs) as a method for efficiently and approximately sampling from the exponentially tilted kernels that arise from KL-regularized reward alignment. Using only black-box sampling access to the pretrained mode
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