Import AI
Jun 8
Import AI 460: Reward hacking society, RSI data from Anthropic; and RL-based quadcopter racing
When will markets price the singularity?
Reinforcement learning
Policy optimisation, reward modelling and RLHF — how models are trained by feedback rather than by labels.
Hugging Face Trending Papers
Jun 8
One Model, Multiple Goals: Adaptive Multi-Objective Learning for E-commerce Dialogue Systems
Dialogue systems in e-commerce scenarios often need to satisfy multiple objectives: accurately reasoning over user profiles (e. g.
Hugging Face Trending Papers
Jun 8
Temporal-Aware Reasoning Optimization for Video Temporal Grounding
Multi-modal Large Language Models (MLLMs) have achieved remarkable progress in video temporal grounding with reinforcement learning for generating reasoning paths. However, existing models often produce superficial reasoning, which offers limited guidance for precise temporal localization.
Hugging Face Trending Papers
Jun 8
DynaCF: Mitigating Shortcut Learning in Reward Models via Dynamic Counterfactual Sensitivity
Reward models trained from pairwise preferences often exploit superficial shortcut cues rather than learning true response quality. We propose DynaCF, a dynamic reweighting framework for mitigating shortcut learning in reward model training.
arXiv Machine Learning
Jun 8
High entropy leads to symmetry-equivariant policies in Dec-POMDPs
arXiv:2511. 22581v5 Announce Type: replace Abstract: We prove that in any Dec-POMDP, sufficiently high entropy regularization ensures that the policy gradient flow with tabular softmax parametrization always converges, for any initialization, to the same joint policy, and that this joint policy is equivariant w.
arXiv Machine Learning
Jun 8
Performance Variation in Deep Reinforcement Learning
arXiv:2606. 06746v1 Announce Type: new Abstract: Deep reinforcement learning (RL) algorithms often suffer from low run-to-run robustness, manifesting as significant performance variation across independent runs of identically configured agents.
arXiv AI
Jun 8
Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach
arXiv:2510. 09041v3 Announce Type: replace-cross Abstract: Deep reinforcement learning (DRL) has demonstrated remarkable success in developing autonomous driving policies.
arXiv AI
Jun 8
Beyond Output Matching: Preserving Internal Geometry in NVFP4 LLM Distillation
arXiv:2606. 05682v2 Announce Type: replace Abstract: Demand for low-precision inference, including NVFP4-based approaches, has grown as large language models are increasingly deployed in latency and cost constrained production environments.
arXiv AI
Jun 8
Superintelligent Retrieval Agent: The Next Frontier of Agentic Retrieval
arXiv:2605. 06647v2 Announce Type: replace-cross Abstract: Retrieval-augmented agents are increasingly the interface to large knowledge bases, yet most treat retrieval as a black box: they issue exploratory queries, inspect snippets, and reformulate until evidence emerges.
arXiv Machine Learning
Jun 8
AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning
arXiv:2512. 13278v2 Announce Type: replace-cross Abstract: Agentic reinforcement learning has advanced large language models (LLMs) to reason through long chain-of-thought trajectories while interleaving external tool use.
arXiv AI
Jun 8
The Sim-to-Real Gap of Foundation Model Agents: A Unified MDP Perspective
arXiv:2606. 07017v1 Announce Type: new Abstract: Foundation model agents are increasingly deployed for real-world decision-making, but suffer from the sim-to-real gap.
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 AI
Jun 8
Should You Use Your Large Language Model to Explore or Exploit?
arXiv:2502. 00225v4 Announce Type: replace-cross Abstract: We evaluate the ability of the current generation of large language models (LLMs) to help a decision-making agent facing an exploration-exploitation tradeoff.
arXiv AI
Jun 8
Robust Instruction Compliance in Cooperative Multi-Agent Reinforcement Learning
arXiv:2605. 12655v2 Announce Type: replace Abstract: Multi-agent reinforcement learning (MARL) in real-world use cases may need to adapt to external natural language instructions that interrupt ongoing behavior and conflict with long-horizon objectives.
arXiv AI
Jun 8
ViVa: A Video-Generative Value Model for Robot Reinforcement Learning
arXiv:2604. 08168v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models have advanced robot manipulation through large-scale pretraining, but real-world deployment remains challenging due to partial observability and delayed feedback.
arXiv Machine Learning
Jun 8
Depth over Fidelity in Fixed-Budget Noisy Evolution Strategies
arXiv:2606. 06555v1 Announce Type: cross Abstract: Noisy evolution strategies under fixed evaluation budgets face a depth-fidelity trade-off: spending evaluations to denoise intra-generation rankings reduces the number of distribution updates the optimizer can execute.
arXiv AI
Jun 8
MacArena: Benchmarking Computer Use Agents on an Online macOS Environment
arXiv:2606. 06560v1 Announce Type: cross Abstract: Computer-use agents (CUAs) operate graphical user interfaces (GUIs) through vision and control primitives, and their capabilities have advanced rapidly, driven in part by standardized online evaluation benchmarks such as OSWorld, which serve both as evaluation tools and as training environments for reinforcement learning.
arXiv AI
Jun 8
StainFlow: Entity-Stain Tracking and Evidence Linking for Process Rewards in GUI Agents
arXiv:2606. 07027v1 Announce Type: new Abstract: Reinforcement Learning (RL) has become a promising approach for improving GUI Agents in long-horizon, stochastic digital environments, but trajectory-level success feedback is too sparse to provide reliable credit assignment for intermediate exploration steps.
arXiv AI
Jun 8
On the Geometry of On-Policy Distillation
arXiv:2606. 07082v1 Announce Type: cross Abstract: On-policy distillation (OPD) is increasingly used to improve large language model reasoning, but its training dynamics remain poorly understood.
arXiv Machine Learning
Jun 8
AdaJudge: Adaptive Multi-Perspective Judging for Reward Modeling
arXiv:2601. 08097v2 Announce Type: replace-cross Abstract: Reward modeling is essential for aligning large language models with human preferences, yet predominant architectures rely on a static pooling strategy to condense sequences into scalar scores.