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🧠 AI🟢 BullishImportance 7/10
Inference-time Alignment in Continuous Space
arXiv – CS AI|Yige Yuan, Teng Xiao, Li Yunfan, Bingbing Xu, Shuchang Tao, Yunqi Qiu, Huawei Shen, Xueqi Cheng|
🤖AI Summary
Researchers propose Simple Energy Adaptation (SEA), a new algorithm for aligning large language models with human feedback at inference time. SEA uses gradient-based sampling in continuous latent space rather than searching discrete response spaces, achieving up to 77.51% improvement on AdvBench and 16.36% on MATH benchmarks.
Key Takeaways
- →SEA addresses limitations of existing inference-time alignment methods that struggle with weak base policies or small candidate sets.
- →The algorithm adapts responses via gradient-based sampling in continuous latent space instead of expensive discrete space searches.
- →SEA formulates inference as iterative optimization on an energy function over actions in continuous space.
- →Performance improvements include 77.51% relative improvement on AdvBench and 16.36% on MATH benchmarks.
- →The research code is publicly available on GitHub for implementation and further development.
#llm#alignment#inference#optimization#machine-learning#gradient-based#continuous-space#sea-algorithm
Read Original →via arXiv – CS AI
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