Tracked topic
…should the teacher always see the full reference reasoning? We identify a teacher-side exposure mismatch, where fully privileged teacher signals can be too strong for the student’s current competence. Instead…
…quality, a Follower that learns to follow evolved instructions, and a Judger that provides reward signals for reinforcement learning . The Instructor and Follower are alternately trained and co-evolve throughout the process…
…Empirically, EnergyFlow achieves state-of-the-art imitation performance on various manipulation tasks while providing an effective reward signal for downstream reinforcement learning that outperforms both adversarial IRL methods and likelihood-based…
…Generated by Qwen/Qwen2.5-Coder-32B-Instruct Reward models (RMs) provide critical feedback signals for LLM post-training, notably in reinforced fine-tuning (RFT) and reinforcement learning (RL) pipelines. However, current…
…Further analysis uncovers sharp capability boundaries across task domains, divergent behavioral strategies among models, and a disconnect between token consumption and output quality , providing fine-grained diagnostic signals beyond what aggregate metrics…
…if executing the payload derails the user's legitimate task, the resulting failure signal invites inspection of the skill. We therefore evaluate attacks by Attack Success Rate , which requires the injected payload…
…TASTE utilizes an Adaptive Contrastive n-gram model trained on LLM-judged validity signals . This enables sampling valid tool sequences that cover a vast range of tool combinations. TASTE then selects representative…
…Both turn out to encode linearly separable hallucination signals, concentrated in a sparse subset of features that strengthen in deeper layers. Steering activations away from these directions at inference yields large drops…
…the previous model, applies targeted enhancement to these regions, and improves the reliability of supervision signals. It further adopts a progressive post-training recipe based on curated data selection and reinforcement learning…
…Existing approaches often introduce dense intermediate supervision, such as process reward models or auxiliary self-supervised signals, which increases supervision and tuning complexity and may limit generalization across tasks and domains. We…