arXiv AI
POETS: Uncertainty-Aware LLM Optimization via Compute-Efficient Policy Ensembles
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arXiv AI
Jun 3
Using Reward Uncertainty to Induce Diverse Behaviour in Reinforcement Learning
arXiv:2606. 03962v1 Announce Type: cross Abstract: Classical reinforcement learning (RL) typically seeks a deterministic policy that maximizes the expected sum of a scalar reward.
arXiv Machine Learning
Jun 8
Uncertainty-Aware LLM-Guided Policy Shaping for Sparse-Reward Reinforcement Learning
arXiv:2606. 06673v1 Announce Type: new Abstract: Sparse rewards and heterogeneous task sequences remain persistent challenges in Reinforcement Learning (RL), often resulting in slow convergence, weak generalization, and inefficient exploration.
arXiv Machine Learning
Jun 18
Be Your Own Teacher: Steering Protein Language Models via Unsupervised Reward Optimization
arXiv:2606. 18961v1 Announce Type: new Abstract: Protein language models (PLMs) have emerged as powerful tools for controllable biomolecular design, yet their post-training adaptation typically relies on costly wet-lab validation or curated preference datasets.
arXiv Machine Learning
Oct 16
The Tilted Sampling Problem: Optimism, Posterior Sampling, and Logarithmic Regret
The paper introduces the tilted sampling problem, where an online learner aims to sample from a Gibbs distribution defined by an unknown reward function. It defines sampling regret as the temperature‑normalized cumulative KL divergence between the learner’s distribution and the target Gibbs distribution, linking it to entropy‑regularized reward maximization. The authors develop sampling analogues of UCB and Thompson sampling, proving that sampling regret can achieve logarithmic growth in time, unlike standard regret which scales as ×O(√T).
Hugging Face Trending Papers
Jun 17
Be Your Own Teacher: Steering Protein Language Models via Unsupervised Reward Optimization
Protein language models (PLMs) have emerged as powerful tools for controllable biomolecular design, yet their post-training adaptation typically relies on costly wet-lab validation or curated preference datasets. To overcome this supervision bottleneck, we introduce unsupervised reward optimization of PLMs, a comprehensive framework for steerable protein generation without ground-truth labels.
Hugging Face Trending Papers
Jun 2
Using Reward Uncertainty to Induce Diverse Behaviour in Reinforcement Learning
Classical reinforcement learning (RL) typically seeks a deterministic policy that maximizes the expected sum of a scalar reward. Yet, modern applications such as language model fine-tuning or scientific discovery demand diversity.