Author

Minsu Jang

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#machine learning Preprint Aug 2026

Demystifying Reinforcement Learning Post-Training of Language Models

This work deconstructs the RL post-training algorithm, investigating each step to clarify what is actually happening beneath the surface, and uses the entropy of the policy's output distribution as a lens to compare the distributions learned through pretraining, SFT, and RL post-training, revealing how each stage shapes model certainty.

D. Clay, Saket Gollapudi, Sankar V Harilal et al. · 0 citations

#artificial intelligence Review Aug 2026

The Lifecycle of LLM-as-a-Judge for Large-Scale Recommendation Explanations

This work argues that an LLM judge running in a production system is better understood as having a lifecycle: it must be built, trained, deployed, and continuously maintained as the surrounding data evolves, and each phase poses distinct technical and operational challenges.

Emma Kong, J. Tan, Ishan Gupta et al. · 0 citations