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cs.LG2026
Tracing Uncertainty in Language Model "Reasoning"
Nils Grünefeld, Bertram Højer, Philipp Mondorf +5
Language model (LM) "reasoning", commonly described as Chain-of-Thought or test-time scaling, often improves benchmark performance, but the dynamics underlying this process remain…
cs.LG2024
Towards Interpretable Reinforcement Learning with Constrained Normalizing Flow Policies
Finn Rietz, Erik Schaffernicht, Stefan Heinrich +1
Reinforcement learning policies are typically represented by black-box neural networks, which are non-interpretable and not well-suited for safety-critical domains. To address both…