activity
20242026
most citedBenefits and Pitfalls of Reinforcement Learning for Language Model Planning: A Theoretical Perspective

1 citations · 1 across the 5 of their papers we have counts for

collaborators
Showing cs.CLShow all

1 paper · 1 filter

cs.CL2026

PaT: Planning-after-Trial for Efficient Test-Time Code Generation

Youngsik Yoon, Sungjae Lee, Seockbean Song +3

Beyond training-time optimization, scaling test-time computation has emerged as a key paradigm to extend the reasoning capabilities of Large Language Models (LLMs). However, most e…