Papers4

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#Group Relative Policy Optimization (GRPO)

WideSeek: Advancing Wide Research via Multi-Agent Scaling

Beginner

Ziyang Huang, Haolin Ren et al.Feb 2arXiv

The paper tackles a new kind of search called Wide Research, where an AI must gather lots of related facts under complex rules and put them into a clean table.

#Wide Research#General Broad Information Seeking#Knowledge Graph

Alternating Reinforcement Learning for Rubric-Based Reward Modeling in Non-Verifiable LLM Post-Training

Intermediate

Ran Xu, Tianci Liu et al.Feb 2arXiv

The paper introduces Rubric-ARM, a system that teaches two AI helpers—a rubric maker and a judge—to learn together using reinforcement learning so they can better decide which answers people would prefer.

#Rubric-based reward modeling#LLM-as-a-judge#Alternating reinforcement learning

GameTalk: Training LLMs for Strategic Conversation

Intermediate

Victor Conchello Vendrell, Max Ruiz Luyten et al.Jan 22arXiv

Large language models usually get judged one message at a time, but many real tasks need smart planning across a whole conversation.

#strategic conversation#reinforcement learning for LLMs#multi-turn dialogue

Ministral 3

Beginner

Alexander H. Liu, Kartik Khandelwal et al.Jan 13arXiv

Ministral 3 is a new family of small-but-mighty AI language models (3B, 8B, 14B) that learn from a larger model using a step-by-step tutoring method called Cascade Distillation.

#Cascade Distillation#Model pruning#Logit distillation