๐ŸŽ“How I Study AIHISA
๐Ÿ“–Read
๐Ÿ“„Papers๐Ÿ“ฐBlogs๐ŸŽฌCourses
๐Ÿ’กLearn
๐Ÿ›ค๏ธPaths๐Ÿ“šTopics๐Ÿ’กConcepts๐ŸŽดShorts
๐ŸŽฏPractice
๐ŸงฉProblems๐ŸŽฏPrompts๐Ÿง Review
Search
How I Study AI - Learn AI Papers & Lectures the Easy Way

Papers5

AllBeginnerIntermediateAdvanced
All SourcesarXiv
#policy gradient

Reinforced Attention Learning

Intermediate
Bangzheng Li, Jianmo Ni et al.Feb 4arXiv

This paper teaches AI to pay attention better by training its focus, not just its words.

#Reinforced Attention Learning#attention policy#multimodal LLM

BatCoder: Self-Supervised Bidirectional Code-Documentation Learning via Back-Translation

Intermediate
Jingwen Xu, Yiyang Lu et al.Jan 30arXiv

BatCoder teaches a code model to write both code and its documentation by doing a round trip: from code to docs and back to code.

#back-translation#self-supervised learning#reinforcement learning for code

Learning to Discover at Test Time

Intermediate
Mert Yuksekgonul, Daniel Koceja et al.Jan 22arXiv

This paper shows how to keep training a language model while it is solving one hard, real problem, so it can discover a single, truly great answer instead of many average ones.

#test-time training#reinforcement learning#entropic objective

JudgeRLVR: Judge First, Generate Second for Efficient Reasoning

Intermediate
Jiangshan Duo, Hanyu Li et al.Jan 13arXiv

JudgeRLVR teaches a model to be a strict judge of answers before it learns to generate them, which trims bad ideas early.

#RLVR#judge-then-generate#discriminative supervision

Beyond Token-level Supervision: Unlocking the Potential of Decoding-based Regression via Reinforcement Learning

Intermediate
Ming Chen, Sheng Tang et al.Dec 6arXiv

The paper shows that making a model write a number as a sequence of digits and then grading the whole number at the end works better than grading each digit separately.

#decoding-based regression#sequence-level reward#reinforcement learning