3 papers
cs.AI2026
Solver-Aware Decompositions for Programming-by-Example: When Dividing Requires Knowing how to Conquer
Janis Zenkner, Tobias Sesterhenn, Tim Grams +1
Decomposition-based Programming-by-example (PBE) scales performance by splitting tasks into subtasks that a learned synthesizer solves: a decomposer predicts intermediate subgoals,…
cs.LG2025
Disentangling Exploration of Large Language Models by Optimal Exploitation
Tim Grams, Patrick Betz, Sascha Marton +2
Exploration is a crucial skill for in-context reinforcement learning in unknown environments. However, it remains unclear if large language models can effectively explore a partial…
cs.LG2025
Mitigating Information Loss in Tree-Based Reinforcement Learning via Direct Optimization
Sascha Marton, Tim Grams, Florian Vogt +3
Reinforcement learning (RL) has seen significant success across various domains, but its adoption is often limited by the black-box nature of neural network policies, making them d…