…However, the underlying mechanism that truly drives performance remains obscured behind intricate system designs. In this paper, we propose HeavySkill , a perspective that views heavy thinking not only as a minimal execution…
…I've been studying SmolLM3's dual-mode training approach and have a technical question about the choice of Anchored Preference Optimization (APO) over Group Relative Policy Optimization (GRPO) for handling reasoning…
…Ziyu Guo , , , Abstract ATLAS presents a visual reasoning framework that combines agentic operations and latent representations using functional tokens, enabling efficient training and improved performance on complex benchmarks. AI-generated summary Visual…
…Crucially, we leverage these fine-grained attribution signals to guide downstream prompt optimization , establishing a closed-loop system that automatically corrects faults and boosts end-task performance by up to 7.62…
…AI-generated summary Token serves as the fundamental unit of computation in modern autoregressive models , and generation length directly influences both inference cost and reasoning performance. Despite its importance, existing approaches lack…
…Guided and unguided rollouts are then jointly optimized via mixed-policy training , internalizing the exploration gains back into the unguided policy. Motivated by a theoretical and empirical analysis of the benefit-risk…
…RLDX-1 further combines this architecture with system-level design choices, including synthesizing training data for rare manipulation scenarios, learning procedures specialized for human-like manipulation, and inference optimizations for real-time…
…Then, we introduce Group Contrastive Preference Optimization (GCPO), a reinforcement learning algorithm that leverages human pairwise preference data to reinforce our pointwise RRM. After building the RRM, we use GRPO to train…
…Alan Arazi , , Shoham Grunblat , , Elad Hoffer , Gioia Blayer , , Lennart Purucker , , , Abstract Multimodal tabular learning benchmarks reveal that task-specific embedding tuning improves performance over frozen pretrained embeddings, particularly when modalities provide complementary…
…Driven by its efficient architecture design and inference optimization, the model can perform real-time full-duplex omni-modal interaction on edge devices with less than 12GB RAM cost. View arXiv page…