Negative Advantages Is a Double-Edged Sword: Calibrating advantages in GRPO for Search Agents
cs.CL, cs.AI
Submitted: 2026-04-20
Updated: 2026-10-12
Code: https://github.com/wujwyi/CalibAdv
Terminology
Sources
- On Group Relative Policy Optimization Collapse in Agent Search: The Lazy Likelihood-Displacement
- Group-in-Group Policy Optimization for LLM Agent Training
- Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps
- Tree Search for LLM Agent Reinforcement Learning
- Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
- TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
- Dense Passage Retrieval for Open-Domain Question Answering
- When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories
- Measuring and Narrowing the Compositionality Gap in Language Models
- Defeating the Training-Inference Mismatch via FP16
- Qwen2.5 Technical Report
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- Deep Research: A Systematic Survey
- R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning
- ZeroSearch: Incentivize the Search Capability of LLMs without Searching
- MuSiQue: Multihop Questions via Single-hop Question Composition
- Text Embeddings by Weakly-Supervised Contrastive Pre-training
- RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning
- StepSearch: Igniting LLMs Search Ability via Step-Wise Proximal Policy Optimization
- Reinforcing Multi-Turn Reasoning in LLM Agents via Turn-Level Reward Design
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