PPaperPicks

Jingang Wang

43 papers at tracked venues · 28 at CORE A* · active 20242026

Venues

Frequent coauthors

Papers

  1. BAPO: Boundary-Aware Policy Optimization for Reliable Agentic Search
  2. BaseCal: Unsupervised Confidence Calibration via Base Model Signals
  3. From Mathematical Reasoning to Code: Generalization of Process Reward Models in Test-Time Scaling
  4. LANG: Reinforcement Learning for Multilingual Reasoning with Language-Adaptive Hint Guidance
  5. Large-Scale Diverse Synthesis for Mid-Training
  6. LinkQA: Synthesizing Diverse QA from Multiple Seeds Strongly Linked by Knowledge Points
  7. MTR-Suite: A Framework for Evaluating and Synthesizing Conversational Retrieval Benchmarks
  8. Rethinking the Sampling Criteria in Reinforcement Learning for LLM Reasoning: A Competence-Difficulty Alignment Perspective
  9. Scaling and Transferability of Annealing Strategies in Large Language Model Training
  10. The Evolution of Thought: Tracking LLM Overthinking via Reasoning Dynamics Analysis
  11. Unlocking Implicit Experience: Synthesizing Tool-Use Trajectories from Text
  12. AgentRefine: Enhancing Agent Generalization through Refinement Tuning
  13. Dynamic Fisher-weighted Model Merging via Bayesian Optimization
  14. Earlier Tokens Contribute More: Learning Direct Preference Optimization From Temporal Decay Perspective
  15. FIRE: Flexible Integration of Data Quality Ratings for Effective Pretraining
  16. FRAME: Boosting LLMs with A Four-Quadrant Multi-Stage Pretraining Strategy
  17. IIET: Efficient Numerical Transformer via Implicit Iterative Euler Method
  18. Investigating and Scaling up Code-Switching for Multilingual Language Model Pre-Training
  19. Jailbreak LLMs through Internal Stance Manipulation
  20. Leveraging Unpaired Feedback for Long-Term LLM-based Recommendation Tuning
  21. Multi-Programming Language Sandbox for LLMs
  22. NeedleInATable: Exploring Long-Context Capability of Large Language Models towards Long-Structured Tables
  23. Preference Curriculum: LLMs Should Always Be Pretrained on Their Preferred Data
  24. ReMamba: Equip Mamba with Effective Long-Sequence Modeling
  25. Revisiting Scaling Laws for Language Models: The Role of Data Quality and Training Strategies
  26. SEAS: Self-Evolving Adversarial Safety Optimization for Large Language Models
  27. SampleMix: A Sample-wise Pre-training Data Mixing Strategy by Coordinating Data Quality and Diversity
  28. TCPO: Thought-Centric Preference Optimization for Effective Embodied Decision-making
  29. Too Consistent to Detect: A Study of Self-Consistent Errors in LLMs
  30. C-ICL: Contrastive In-context Learning for Information Extraction
  31. CoSTA: End-to-End Comprehensive Space-Time Entanglement for Spatio-Temporal Video Grounding
  32. DolphCoder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning
  33. EAVE: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interaction
  34. Forgetting Curve: A Reliable Method for Evaluating Memorization Capability for Long-Context Models
  35. Graph-Structured Speculative Decoding
  36. How Do Your Code LLMs perform? Empowering Code Instruction Tuning with Really Good Data
  37. Let's Ask GNN: Empowering Large Language Model for Graph In-Context Learning
  38. MCL-NER: Cross-Lingual Named Entity Recognition via Multi-View Contrastive Learning
  39. Predictor-Corrector Enhanced Transformers with Exponential Moving Average Coefficient Learning
  40. Scaling Laws Across Model Architectures: A Comparative Analysis of Dense and MoE Models in Large Language Models
  41. Speculative Decoding via Early-exiting for Faster LLM Inference with Thompson Sampling Control Mechanism
  42. Unleashing Region Understanding in Intermediate Layers for MLLM-based Referring Expression Generation
  43. What Makes Quantization for Large Language Model Hard? An Empirical Study from the Lens of Perturbation