6 papers · 1 filter
Where Rollouts Begin: Low-Load, High-Leverage First-Token Diversification for RLVR
Soeun Kim, Albert No
Reinforcement Learning with Verifiable Rewards (RLVR) trains reasoning models without labeled trajectories, relying on grouped rollouts to expose the policy to alternative reasonin…
A Theoretical Analysis of Why Masked Diffusion Models Mitigate the Reversal Curse
Moongyu Jeon, Sangwoo Shin, BumJun Kim +2
Autoregressive language models (ARMs) suffer from the reversal curse: after learning '' is ,'' they often fail on the reverse query '' is .'' Masked diffusion language…
JUMP: Single-Pass Membership Inference on Fine-Tuned Diffusion Language Models
Yeachan Jun, Albert No
Public open-weight language models are often fine-tuned on private or domain-specific data before deployment, creating a need to audit whether individual records were used during a…
BenchPreS: A Benchmark for Context-Aware Personalized Preference Selectivity of Persistent-Memory LLMs
Sangyeon Yoon, Sunkyoung Kim, Hyesoo Hong +5
Large language models (LLMs) increasingly store user preferences in persistent memory to support personalization across interactions. However, in third-party communication settings…
SAFEPATH: Preventing Harmful Reasoning in Chain-of-Thought via Early Alignment
Wonje Jeung, Sangyeon Yoon, Minsuk Kahng +1
Large Reasoning Models (LRMs) have become powerful tools for complex problem solving, but their structured reasoning pathways can lead to unsafe outputs when exposed to harmful pro…
Rainbow Padding: Mitigating Early Termination in Instruction-Tuned Diffusion LLMs
Bumjun Kim, Dongjae Jeon, Dueun Kim +2
Diffusion large language models (dLLMs) have emerged as a promising alternative to autoregressive models, offering flexible generation orders and strong performance on complex reas…