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Machine Learning
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Newest papers, page 188
MetaCurate: Evolving Curation Filters for High-Quality Agentic Coding Data
Supervised fine-tuning on agent trajectories is an effective way to improve coding agents, but selecting useful training examples requires more than identifying successful runs.Local proxies for memory-efficient training
Local learning algorithms can reshape resource requirements of artificial intelligence.Grounded Credit Assignment in Reinforcement Learning for Multi-Turn Search Agents
Outcome-reward reinforcement learning such as GRPO gives every token of a search-agent rollout the same advantage, although the rollout exercises two capabilities: finding evidence and using it.CIIR - Class-Indexed Interface Representation Learning
Multimodal learning commonly assumes paired or synchronized observations, yet independently collected datasets rarely provide such correspondence.Nearly Linear Private Learning of Littlestone Classes
Differentially private learning aims to learn from data while protecting individual training examples.Explaining Chess and Go in Natural Language
Modern chess engines play far beyond human ability, often choosing moves whose underlying ideas are difficult for humans to understand.Structural Affinity: An Algorithm for Coherent Cage-Based Shape Design
Cage-based deformation (CBD) parametrises 3D geometry through a small set of control handles, effectively enabling a variety of tasks from animation and interactive modelling to tool-based…Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning
Reinforcement learning with verifiable rewards has become a scalable post-training paradigm for large language models.A Stronger Teacher Within: Strengthening MoE Models via On-Policy Bidirectional Distillation
Reinforcement Learning with Verifiable Rewards (RLVR) has substantially improved Mixture-of-Experts (MoE) LLMs, but its outcome-level rewards provide only sparse supervision.LangNCO: Compiling Natural-Language Constraints into Inference-Time Interventions for Neural Combinatorial Optimization
Neural combinatorial optimization (NCO) rapidly produces high-quality solutions for problem structures encountered during training, but operational requirements may change after deployment and be…Proportionality in Ranking Compression
Motivated by applications in reinforcement learning from human feedback, we consider a setting where a given collection of rankings needs to be compressed into a smaller collection of rankings.Non-Parametric Optimization for Scalable Learning in Stochastic Decision Problems
Stochastic optimization (SO) plays a central role in decision-making under uncertainty.Process Reward Modeling with Reliability- and Conservatism-Aware Bayesian Belief
Process Reward Models (PRMs) play a central role in test-time scaling for large language model (LLM) reasoning.Characterising and Analysing Retrieval-Augmented Generation Techniques
The advent of Large Language Models (LLMs) across many application domains has increased the need for Retrieval-Augmented Generation (RAG) to reduce hallucinations and improve LLM performance.Response-Informed Value Estimation for Cost-Aware Multi-Turn LLM Routing
Multi-turn LLM routing selects a model at every turn before its response is available, yet task success is revealed only when the interaction ends, leaving each intermediate choice without a label…Anisotropic Representation Dynamics in Transformers
Small differences between internal representations can shrink or grow as they pass through a Transformer layer, affecting how information is preserved and how sensitive the model is to perturbations.Think More or Read More? A Token-Budget Theory of Reasoning with Shared Context
Reasoning systems increasingly improve answers by incorporating additional context into the model's input, from multi-agent debate, where agents read their peers' responses, to self-refinement…Can the Parts Fool the Test? Counterfactual Pairing Cycles for Relational OOD
Relational out-of-distribution errors are easy to describe and hard to evaluate cleanly.FLARE: Physics-Grounded Evaluation of Language-Model Agents for Feeder Restoration with Degraded Observations
Restoring service after distribution-grid faults requires decisions from incomplete and conflicting information while respecting electrical constraints.Sample, Mix, or Verify? Allocating Test-Time Compute under Imperfect Verifiers
A common way to make language models and agents more accurate is to spend more compute at inference time: generate many candidate solutions and submit the one a verifier prefers.