arXiv:2602.00913v3 Announce Type: replace-cross Abstract: Human value detection from single sentences is a sparse, imbalanced multi-label task. We study whether Schwartz higher-order (HO) categories help this setting on ValueEval’24 / ValuesML (74K English sentences) under a compute-frugal budget. Rather than…
arXiv:2604.05112v1 Announce Type: new Abstract: Recent progress in in-context reinforcement learning (ICRL) has demonstrated its potential for training generalist agents that can acquire new tasks directly at inference. Algorithm Distillation (AD) pioneered this paradigm and was subsequently scaled to multi-domain…
arXiv:2604.03885v2 Announce Type: replace-cross Abstract: We address the problem of recovering a time-varying 4D distribution from a sparse sequence of 2D projections – analogous to novel-view synthesis from sparse cameras, but applied to the 4D transverse phase space density $rho(x,p_x,y,p_y)$…
arXiv:2604.05134v1 Announce Type: new Abstract: How can you get a language model to reason in a task it natively struggles with? We study how reasoning evolves in a language model — from supervised fine-tuning (SFT) to reinforcement learning (RL) —…
arXiv:2604.06039v1 Announce Type: cross Abstract: Value iteration-type methods have been extensively studied for computing a nearly optimal value function in reinforcement learning (RL). Under a generative sampling model, these methods can achieve sharper sample complexity than policy optimization approaches, particularly…
arXiv:2604.05164v1 Announce Type: new Abstract: As LLM reasoning performance plateau, improving inference-time compute efficiency is crucial to mitigate overthinking and long thinking traces even for simple queries. Prior approaches including length regularization, adaptive routing, and difficulty-based budget allocation primarily focus…
arXiv:2307.02719v4 Announce Type: replace Abstract: Uncertainty sampling is a prevalent active learning algorithm that queries sequentially the annotations of data samples which the current prediction model is uncertain about. However, the usage of uncertainty sampling has been largely heuristic: There…
arXiv:2604.05181v1 Announce Type: new Abstract: Evolution is an extraordinary engine for enzymatic diversity, yet the chemistry it has explored remains a narrow slice of what DNA can encode. Deep generative models can design new proteins that bind ligands, but none…
arXiv:2502.06387v2 Announce Type: replace Abstract: Human-annotated preference data play an important role in aligning large language models (LLMs). In this paper, we study two connected questions: how to monitor the quality of human preference annotators and how to incentivize them…
arXiv:2604.05185v1 Announce Type: new Abstract: Model-based reinforcement learning is attractive for sequential decision-making because it explicitly estimates reward and transition models and then supports planning through simulated rollouts. In offline settings with hidden confounding, however, models learned directly from observational…
