18 papers
ActQuant: Sub-4-bit Action-Guided Quantization for Vision-Language-Action Models
Arash Akbari, Arman Akbari, Masih Eskandar +11
Vision-Language-Action (VLA) models exhibit remarkable action generation for embodied intelligence, but their heavy compute make deployment on edge platforms impractical. Aggressiv…
Survive or Collapse: The Asymmetric Roles of Data Gating and Reward Grounding in Self-Play RL
Sophia Xiao Pu, Zhaotian Weng, Chengzhi Liu +4
Self-play reinforcement learning trains language models on their own generated tasks, co-evolving a proposer and solver without human labels. Recent systems report strong reasoning…
Rethinking Muon Beyond Pretraining: Spectral Failures and High-Pass Remedies for VLA and RLVR
Chongyu Fan, Gaowen Liu, Mingyi Hong +2
Muon is a matrix-aware optimizer that leverages Newton-Schulz (NS) iterations to enforce spectral gradient orthogonalization by driving all singular values of the momentum matrix t…
TIER: Trajectory-Invariant Execution Rewards for Multi-Step Tool Composition
Anay Kulkarni, ChiaEn Lu, Dheeraj Mekala +3
Tool use enables large language models to solve complex tasks through sequences of API calls, yet existing reinforcement learning approaches fail to scale to multi-step composition…
EnactToM: An Evolving Benchmark for Functional Theory of Mind in Embodied Agents
Gurusha Juneja, Dylan Lu, Saaket Agashe +7
Theory of Mind (ToM), the ability to track others epistemic state, makes humans efficient collaborators. AI agents need the same capacity in multi agent settings, yet existing benc…
FAMA: Failure-Aware Meta-Agentic Framework for Open-Source LLMs in Interactive Tool Use Environments
Amir Saeidi, Venkatesh Mishra, Souradeep Mukhopadhyay +4
Large Language Models are being increasingly deployed as the decision-making core of autonomous agents capable of effecting change in external environments. Yet, in conversational…