arxiv stringlengths 10 10 | title stringlengths 9 175 | month stringdate 2025-01-01 00:00:00 2026-06-01 00:00:00 | label stringclasses 7
values | scope stringclasses 2
values | component stringclasses 12
values | label_confidence stringclasses 3
values | is_model_report bool 2
classes | general_method bool 2
classes | included bool 2
classes | methods listlengths 0 5 | n_frameworks_ever int64 0 7 | n_frameworks_at_snapshot int64 0 7 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
2502.12853 | S^2R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning | 2025-02 | rl_application | null | null | medium | false | false | false | [] | 0 | 0 |
2502.12859 | PAFT: Prompt-Agnostic Fine-Tuning | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.12876 | Continuous Learning Conversational AI: A Personalized Agent Framework via A2C Reinforcement Learning | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.12929 | Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking Through Options | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.13019 | Oreo: A Plug-in Context Reconstructor to Enhance Retrieval-Augmented Generation | 2025-02 | rl_application | null | null | high | false | false | false | [] | 0 | 0 |
2502.13092 | Text2World: Benchmarking Large Language Models for Symbolic World Model Generation | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.13137 | Theorem Prover as a Judge for Synthetic Data Generation | 2025-02 | rl_application | null | null | high | false | false | false | [] | 0 | 0 |
2502.13146 | Re-Align: Aligning Vision Language Models via Retrieval-Augmented Direct Preference Optimization | 2025-02 | preference_offline | null | null | high | false | false | false | [] | 0 | 0 |
2502.13187 | A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.13207 | Thinking Outside the (Gray) Box: A Context-Based Score for Assessing Value and Originality in Neural Text Generation | 2025-02 | rl_method | general | other | high | false | true | false | [] | 0 | 0 |
2502.13374 | Task-agnostic Prompt Compression with Context-aware Sentence Embedding and Reward-guided Task Descriptor | 2025-02 | rl_application | null | null | high | true | false | false | [] | 0 | 0 |
2502.13388 | Reflection of Episodes: Learning to Play Game from Expert and Self Experiences | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.13389 | Reasoning with Reinforced Functional Token Tuning | 2025-02 | rl_method | general | sampling | high | false | true | false | [] | 0 | 0 |
2502.13417 | RLTHF: Targeted Human Feedback for LLM Alignment | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.13430 | Vision-Based Generic Potential Function for Policy Alignment in Multi-Agent Reinforcement Learning | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.13475 | LLM should think and action as a human | 2025-02 | rl_application | null | null | high | false | false | false | [] | 0 | 0 |
2502.13516 | SPPD: Self-training with Process Preference Learning Using Dynamic Value Margin | 2025-02 | preference_offline | null | null | medium | false | false | false | [] | 0 | 0 |
2502.13723 | Direct Value Optimization: Improving Chain-of-Thought Reasoning in LLMs with Refined Values | 2025-02 | rl_method | general | offpolicy | high | false | true | false | [] | 0 | 0 |
2502.13820 | Scoring Verifiers: Evaluating Synthetic Verification for Code and Reasoning | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.14096 | Aligned Multi Objective Optimization | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.14204 | On-the-fly Preference Alignment via Principle-Guided Decoding | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.14289 | Drift: Decoding-time Personalized Alignments with Implicit User Preferences | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.14340 | Earlier Tokens Contribute More: Learning Direct Preference Optimization From Temporal Decay Perspective | 2025-02 | preference_offline | null | null | high | false | false | false | [] | 0 | 0 |
2502.14400 | HPS: Hard Preference Sampling for Human Preference Alignment | 2025-02 | preference_offline | null | null | high | false | false | false | [] | 0 | 0 |
2502.14496 | Advancing Language Multi-Agent Learning with Credit Re-Assignment for Interactive Environment Generalization | 2025-02 | preference_offline | null | null | low | false | false | false | [] | 0 | 0 |
2502.14499 | MLGym: A New Framework and Benchmark for Advancing AI Research Agents | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.14565 | ReVISE: Learning to Refine at Test-Time via Intrinsic Self-Verification | 2025-02 | preference_offline | null | null | medium | false | false | false | [] | 0 | 0 |
2502.14643 | Length-Controlled Margin-Based Preference Optimization without Reference Model | 2025-02 | preference_offline | null | null | high | false | false | false | [] | 0 | 0 |
2502.14669 | AlphaMaze: Enhancing Large Language Models' Spatial Intelligence via GRPO | 2025-02 | rl_application | null | null | high | false | false | false | [] | 0 | 0 |
2502.14768 | Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning | 2025-02 | rl_application | null | null | high | false | false | false | [] | 0 | 0 |
2502.14905 | Think Inside the JSON: Reinforcement Strategy for Strict LLM Schema Adherence | 2025-02 | rl_application | null | null | high | false | false | false | [] | 0 | 0 |
2502.15119 | CurricuVLM: Towards Safe Autonomous Driving via Personalized Safety-Critical Curriculum Learning with Vision-Language Models | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.15145 | Projection Optimization: A General Framework for Multi-Objective and Multi-Group RLHF | 2025-02 | rl_method | general | multi_reward | high | false | true | false | [] | 0 | 0 |
2502.15214 | The Evolving Landscape of LLM- and VLM-Integrated Reinforcement Learning | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.15631 | The relationship between reasoning and performance in large language models--o3 (mini) thinks harder, not longer | 2025-02 | analysis | null | null | high | false | false | false | [] | 0 | 0 |
2502.15760 | Digi-Q: Learning Q-Value Functions for Training Device-Control Agents | 2025-02 | rl_method | embedded | offpolicy | high | false | false | false | [] | 0 | 0 |
2502.15763 | Hybrid Offline-online Scheduling Method for Large Language Model Inference Optimization | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.16182 | IPO: Your Language Model is Secretly a Preference Classifier | 2025-02 | preference_offline | null | null | high | false | false | false | [] | 0 | 0 |
2502.16195 | Statistical Inference in Reinforcement Learning: A Selective Survey | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.16198 | An Autonomous Network Orchestration Framework Integrating Large Language Models with Continual Reinforcement Learning | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.16343 | Exploring Sentiment Manipulation by LLM-Enabled Intelligent Trading Agents | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.16852 | Improving LLM General Preference Alignment via Optimistic Online Mirror Descent | 2025-02 | rl_method | general | other | high | false | true | false | [] | 0 | 0 |
2502.16863 | Leveraging Large Language Models for Effective and Explainable Multi-Agent Credit Assignment | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.16940 | Reasoning Does Not Necessarily Improve Role-Playing Ability | 2025-02 | analysis | null | null | medium | false | false | false | [] | 0 | 0 |
2502.17011 | Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.17055 | GradientStabilizer:Fix the Norm, Not the Gradient | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.17387 | Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.17507 | C2-DPO: Constrained Controlled Direct Preference Optimization | 2025-02 | preference_offline | null | null | high | false | false | false | [] | 0 | 0 |
2502.17515 | Towards User-level Private Reinforcement Learning with Human Feedback | 2025-02 | rl_method | general | other | low | false | true | false | [] | 0 | 0 |
2502.17701 | From Perceptions to Decisions: Wildfire Evacuation Decision Prediction with Behavioral Theory-informed LLMs | 2025-02 | other | null | null | low | false | false | false | [] | 0 | 0 |
2502.17721 | Aligning Compound AI Systems via System-level DPO | 2025-02 | preference_offline | null | null | high | false | false | false | [] | 0 | 0 |
2502.17823 | A General Framework to Enhance Fine-tuning-based LLM Unlearning | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.17840 | A Combinatorial Identities Benchmark for Theorem Proving via Automated Theorem Generation | 2025-02 | rl_application | null | null | low | false | false | false | [] | 0 | 0 |
2502.18008 | NotaGen: Advancing Musicality in Symbolic Music Generation with Large Language Model Training Paradigms | 2025-02 | preference_offline | null | null | high | true | false | false | [] | 0 | 0 |
2502.18439 | MAPoRL: Multi-Agent Post-Co-Training for Collaborative Large Language Models with Reinforcement Learning | 2025-02 | rl_method | general | multi_reward | high | false | true | false | [] | 0 | 0 |
2502.18449 | SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution | 2025-02 | rl_application | null | null | high | true | false | false | [] | 0 | 0 |
2502.18699 | MPO: An Efficient Post-Processing Framework for Mixing Diverse Preference Alignment | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.18770 | Reward Shaping to Mitigate Reward Hacking in RLHF | 2025-02 | rl_method | general | other | high | false | true | false | [] | 0 | 0 |
2502.18878 | Learning to Generate Structured Output with Schema Reinforcement Learning | 2025-02 | rl_application | null | null | high | false | false | false | [] | 0 | 0 |
2502.18906 | VEM: Environment-Free Exploration for Training GUI Agent with Value Environment Model | 2025-02 | rl_application | null | null | high | false | false | false | [] | 0 | 0 |
2502.18968 | Know You First and Be You Better: Modeling Human-Like User Simulators via Implicit Profiles | 2025-02 | rl_application | null | null | high | false | false | false | [] | 0 | 0 |
2502.19158 | When Personalization Meets Reality: A Multi-Faceted Analysis of Personalized Preference Learning | 2025-02 | analysis | null | null | high | false | false | false | [] | 0 | 0 |
2502.19230 | Two Heads Are Better Than One: Dual-Model Verbal Reflection at Inference-Time | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.19402 | General Intelligence Requires Reward-based Pretraining | 2025-02 | analysis | null | null | low | false | false | false | [] | 0 | 0 |
2502.19500 | Conversational Planning for Personal Plans | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.19557 | Distill Not Only Data but Also Rewards: Can Smaller Language Models Surpass Larger Ones? | 2025-02 | distillation | null | null | medium | false | false | false | [] | 0 | 0 |
2502.19613 | Self-rewarding correction for mathematical reasoning | 2025-02 | rl_application | null | null | high | false | false | false | [] | 0 | 0 |
2502.19634 | MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language Models (VLMs) via Reinforcement Learning | 2025-02 | rl_application | null | null | high | true | false | false | [] | 0 | 0 |
2502.19655 | Med-RLVR: Emerging Medical Reasoning from a 3B base model via reinforcement Learning | 2025-02 | rl_application | null | null | high | false | false | false | [] | 0 | 0 |
2502.19735 | R1-T1: Fully Incentivizing Translation Capability in LLMs via Reasoning Learning | 2025-02 | rl_application | null | null | high | false | false | false | [] | 0 | 0 |
2502.19794 | ChatMol: A Versatile Molecule Designer Based on the Numerically Enhanced Large Language Model | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.19920 | Pokemon Red via Reinforcement Learning | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.20127 | SoRFT: Issue Resolving with Subtask-oriented Reinforced Fine-Tuning | 2025-02 | rl_application | null | null | high | false | false | false | [] | 0 | 0 |
2502.20548 | Q\sharp: Provably Optimal Distributional RL for LLM Post-Training | 2025-02 | rl_method | general | other | high | false | true | false | [] | 0 | 0 |
2502.20795 | Test-Time Alignment for Large Language Models via Textual Model Predictive Control | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2502.21231 | ByteScale: Efficient Scaling of LLM Training with a 2048K Context Length on More Than 12,000 GPUs | 2025-02 | systems | null | null | high | false | false | false | [] | 0 | 0 |
2502.21321 | LLM Post-Training: A Deep Dive into Reasoning Large Language Models | 2025-02 | other | null | null | high | false | false | false | [] | 0 | 0 |
2503.00030 | RSPO: Regularized Self-Play Alignment of Large Language Models | 2025-03 | rl_method | general | kl | high | false | true | false | [] | 0 | 0 |
2503.00231 | Jawaher: A Multidialectal Dataset of Arabic Proverbs for LLM Benchmarking | 2025-03 | other | null | null | high | false | false | false | [] | 0 | 0 |
2503.00527 | Never too Prim to Swim: An LLM-Enhanced RL-based Adaptive S-Surface Controller for AUVs under Extreme Sea Conditions | 2025-03 | other | null | null | high | false | false | false | [] | 0 | 0 |
2503.00539 | Distributionally Robust Reinforcement Learning with Human Feedback | 2025-03 | rl_method | general | other | high | false | true | false | [] | 0 | 0 |
2503.00735 | LADDER: Self-Improving LLMs Through Recursive Problem Decomposition | 2025-03 | rl_method | general | sampling | medium | false | true | false | [] | 0 | 0 |
2503.00845 | Rewarding Graph Reasoning Process makes LLMs more Generalized Reasoners | 2025-03 | preference_offline | null | null | high | false | false | false | [] | 0 | 0 |
2503.01062 | Offline RLAIF: Piloting VLM Feedback for RL via SFO | 2025-03 | other | null | null | high | false | false | false | [] | 0 | 0 |
2503.01076 | Active Learning for Direct Preference Optimization | 2025-03 | preference_offline | null | null | high | false | false | false | [] | 0 | 0 |
2503.01224 | CE-U: Cross Entropy Unlearning | 2025-03 | other | null | null | high | false | false | false | [] | 0 | 0 |
2503.01233 | PEO: Improving Bi-Factorial Preference Alignment with Post-Training Policy Extrapolation | 2025-03 | other | null | null | medium | false | false | false | [] | 0 | 0 |
2503.01307 | Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs | 2025-03 | analysis | null | null | high | false | false | false | [] | 0 | 0 |
2503.01461 | Marco-o1 v2: Towards Widening The Distillation Bottleneck for Reasoning Models | 2025-03 | distillation | null | null | high | true | false | false | [] | 0 | 0 |
2503.01490 | Improving Retrospective Language Agents via Joint Policy Gradient Optimization | 2025-03 | rl_method | general | offpolicy | high | false | true | false | [] | 0 | 0 |
2503.01491 | What's Behind PPO's Collapse in Long-CoT? Value Optimization Holds the Secret | 2025-03 | rl_method | general | advantage | high | false | true | false | [] | 0 | 0 |
2503.01584 | SENSEI: Semantic Exploration Guided by Foundation Models to Learn Versatile World Models | 2025-03 | other | null | null | high | false | false | false | [] | 0 | 0 |
2503.01895 | Evaluating System 1 vs. 2 Reasoning Approaches for Zero-Shot Time Series Forecasting: A Benchmark and Insights | 2025-03 | other | null | null | high | false | false | false | [] | 0 | 0 |
2503.01923 | Output Length Effect on DeepSeek-R1's Safety in Forced Thinking | 2025-03 | analysis | null | null | low | false | false | false | [] | 0 | 0 |
2503.02077 | M3HF: Multi-agent Reinforcement Learning from Multi-phase Human Feedback of Mixed Quality | 2025-03 | other | null | null | high | false | false | false | [] | 0 | 0 |
2503.02296 | Memorize or Generalize? Evaluating LLM Code Generation with Code Rewriting | 2025-03 | analysis | null | null | high | false | false | false | [] | 0 | 0 |
2503.02379 | Teaching Metric Distance to Discrete Autoregressive Language Models | 2025-03 | other | null | null | high | false | false | false | [] | 0 | 0 |
2503.02623 | Rewarding Doubt: A Reinforcement Learning Approach to Calibrated Confidence Expression of Large Language Models | 2025-03 | rl_application | null | null | high | false | false | false | [] | 0 | 0 |
2503.02832 | AlignDistil: Token-Level Language Model Alignment as Adaptive Policy Distillation | 2025-03 | distillation | null | null | high | false | false | false | [] | 0 | 0 |
2503.02951 | KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding | 2025-03 | other | null | null | high | false | false | false | [] | 0 | 0 |
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