arXiv AI
Jul 29
Structure-aware Relative Policy Optimization for Ranking
arXiv:2607. 25268v1 Announce Type: cross Abstract: Ranking is a fundamental component of modern information access systems.
Reinforcement learning
Policy optimisation, reward modelling and RLHF — how models are trained by feedback rather than by labels.
arXiv AI
Jul 29
A Control System, a Dataset, and a Recipe for Making Frozen LLM Agents Learn a Domain
arXiv:2607. 25415v1 Announce Type: new Abstract: Production LLM agents are increasingly assembled from a frozen model wrapped in a harness: a prompt template, a tool set, a memory/retrieval layer, a planning strategy, and a verification policy.
arXiv AI
Jul 29
AdaKP: Online Adaptive Knowledge-Point Selection for Reasoning-Oriented Reinforcement Learning
arXiv:2607. 24833v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards is a powerful paradigm for eliciting reasoning in large language models, yet it suffers from severe reward sparsity on competition-level mathematics.
arXiv AI
Jul 29
dtControl2+$\varepsilon$: Trading Optimality for Explainability in MDPs via Decision Trees
arXiv:2607. 25925v1 Announce Type: new Abstract: Over the past decade, decision trees have been used to represent controllers (a.
arXiv AI
Jul 29
Calibrated Partial Resets: Preventing Policy Collapse in Continual Reinforcement Learning
arXiv:2607. 24996v1 Announce Type: cross Abstract: Neural networks are hindered by accumulating dormant neurons and loss of expressivity throughout training, particularly in non-stationary data settings, such as continual supervised and reinforcement learning.
arXiv AI
Jul 29
Reinforcement Learning for Code Optimization
arXiv:2607. 25970v1 Announce Type: cross Abstract: RL for code correctness is now established: have the model generate a program, run it against hidden test cases, and reward solutions that pass.
arXiv AI
Jul 29
Toward an Organizational Science of Multi-Agent LLM Systems: Decoupling Who, How, and Which Algorithm
arXiv:2607. 25446v1 Announce Type: new Abstract: Multi-agent frameworks built on large language models (LLMs) routinely entangle three logically distinct concerns: who is on the team (organization), how members align (coordination), and which algorithm fuses their work (collaboration protocol).
arXiv AI
Jul 29
Context Assembly as the Controlled Variable: A Control-Theoretic View of Harness Policies for Frozen LLM Agents
arXiv:2607. 25408v1 Announce Type: new Abstract: A growing body of 2026 work applies control theory to LLM agents: Lyapunov-certified stability for tool-mediated controllers (Prinos et al.
arXiv AI
Jul 29
Similar Models Learn Differently: Final-Window Pretraining Shapes Post-Training Beyond SFT
arXiv:2607. 25063v1 Announce Type: new Abstract: Developers judge a model checkpoint by how it behaves.
arXiv AI
Jul 29
Interactive Reward Agent: GUI Task Evaluation via Environment-State Verification
arXiv:2607. 25904v1 Announce Type: new Abstract: Graphical user interface task evaluation aims to determine whether a GUI agent has successfully completed a user instruction.
arXiv AI
Jul 29
CAST: Game Solvers as Turn-Level Teachers for LLM Agents
arXiv:2607. 25308v1 Announce Type: cross Abstract: Training large language models (LLMs) to act in long-horizon games is a promising step toward generalist decision-making, yet reinforcement learning with verifiable rewards (RLVR) relies on sparse final rewards that reveal little about which decisions determine success.
arXiv AI
Jul 29
LivingArena: Do LLMs Know What Other LLMs Don't? Peer-Probing as Scalable Evaluation
arXiv:2607. 24780v1 Announce Type: new Abstract: Evaluating frontier LLMs is challenging: static benchmarks suffer from contamination and saturation -- leaving users unable to distinguish top models and developers blind to specific failure modes -- while human preference is subjective.
arXiv AI
Jul 29
Speculate While You Reason: Teaching Agents to Predict Their Next Tool Call via Joint Agent-Speculator RL
arXiv:2607. 25816v1 Announce Type: new Abstract: Large language model agents often spend substantial wall-clock time waiting for tool call results.
arXiv AI
Jul 29
HOBA: Hierarchical On-Policy Bidding Agents for Adaptive Online Advertising
arXiv:2607. 24779v1 Announce Type: new Abstract: Online advertising bidding systems typically deploy multiple offline-trained expert models (e.
arXiv AI
Jul 29
PLATO: Pointer Learner for Agent and Task Openness
arXiv:2607. 25082v1 Announce Type: new Abstract: Open agent systems (OASYS) are increasingly prevalent in real-world domains where the sets of agents and tasks change unpredictably over time.
arXiv AI
Jul 29
CoRT: Counterfactual Replay for Token-Level Rubric-Guided Policy Optimization
arXiv:2607. 25659v1 Announce Type: new Abstract: Rubric-based reinforcement learning enriches language model training by evaluating model outputs against explicit criteria.
arXiv AI
Jul 29
Towards Robust Reinforcement Learning for Small-Scale Language Model Agents
arXiv:2607. 25091v1 Announce Type: new Abstract: The alignment of Small Language Models (SLMs) in the 70--500M parameter range using reinforcement learning is often considered unstable, though the underlying failure mechanisms have not been systematically investigated.
arXiv AI
Jul 29
DS@GT ARC at CheckThat! 2026: LLM-Based Trace Ranking and Grouped Reward Modeling for Multilingual Numerical Claim Verification
arXiv:2607. 25069v1 Announce Type: cross Abstract: Automated verification of numerical claims is a challenging problem, as it requires both language understanding and quantitative reasoning.
Hugging Face Trending Papers
Jul 29
Reinforcement Learning on Cost-Constrained Quadrupedal Hardware
Deploying learned control policies on low-cost robotic platforms introduces transport latencies and noisy motor feedback that systematically widens the sim-to-real gap. The chasm of simulation to deployment in hardware lies in the delay of the actuator reaching the commanded position.
Hugging Face Trending Papers
Jul 29
Post-Training at the Edge of Detectability: A Game-Theoretic Approach to Fine-Tuning
Reinforcement learning (RL) fine-tuning is widely used in language model training to improve model performance on a target task while limiting drift from a reference policy. A standard way to balance this trade-off is via a KL-regularized RL objective, although this formulation does not by itself provide a principled way to set the regularization coefficient.