Oyster-II, a new reinforcement learning-based framework for large language model safety, outperforms its predecessor and competitors like Qwen3-14B while balancing safety and utility. The model addres…
Researchers propose Oyster-II, a reinforcement learning framework for constructive safety alignment in large language models, addressing limitations of refusal-based strategies and supervised fine-tun…
Researchers introduced PragReST, a self-supervised framework that uses counterfactual reasoning to improve large language models' pragmatic language understanding. Across four benchmarks, PragReST boo…
Researchers have developed a synthetic contrastive reasoning-trace dataset for multi-table question answering (QA) by generating validated positive and plausible negative traces using heterogeneous la…
Researchers developed a variance-aware reward framework using Group Relative Policy Optimization (GRPO) to improve heart-focused medical question answering in large language models. The approach, whic…
Linear probes applied to Qwen3-14B hidden states achieve 100% accuracy in distinguishing deductive, inductive, and abductive reasoning tasks, but this separation is entirely driven by format confounds…
Researchers at Simorgh have developed a region-aware hybrid retrieval method combining BM25 lexical matching and dense semantic similarity with regional weighting heuristics to improve culturally grou…
Researchers have developed PAT, an adaptive tensor parallelism method that dynamically reconfigures GPU resource allocation during the generation stage of synchronous RLHF training to address the bott…