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20242026
most citedGLM-5: from Vibe Coding to Agentic Engineering

1 citations · 1 across the 10 of their papers we have counts for

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cs.AI2026

How Much, Then Where: Credit-Conserving Action-to-Token Allocation for Multi-Turn Agent Reinforcement Learning

Lichao Ma, Yang Sun, Shuaitao Zhao +9

Credit assignment in multi-turn agent reinforcement learning operates at two levels: assigning trajectory-level credit to actions and distributing each action's credit across its t…

cs.LG2026

Placing Puzzle Pieces Where They Matter: A Question Augmentation Framework for Reinforcement Learning

Yangyi Fang, Jiaye Lin, Xiaoliang Fu +2

Reinforcement learning has become a powerful approach for enhancing large language model reasoning, but faces a fundamental dilemma: training on easy problems can cause overfitting…

cs.LG2026

From to : Taming Divergence in Soft Clipping via Bilateral Decoupled Decay of Probability Gradient Weight

Xiaoliang Fu, Jiaye Lin, Yangyi Fang +6

Reinforcement Learning with Verifiable Rewards (RLVR) has catalyzed a leap in Large Language Model (LLM) reasoning, yet its optimization dynamics remain fragile. Standard algorithm…

cs.LG2026★ 1 cited

GLM-5: from Vibe Coding to Agentic Engineering

GLM-5-Team, :, Aohan Zeng +184

We present GLM-5, a next-generation foundation model designed to transition the paradigm of vibe coding to agentic engineering. Building upon the agentic, reasoning, and coding (AR…

cs.AI2026

Proximity-Based Multi-Turn Optimization: Practical Credit Assignment for LLM Agent Training

Yangyi Fang, Jiaye Lin, Xiaoliang Fu +4

Multi-turn LLM agents are becoming pivotal to production systems, spanning customer service automation, e-commerce assistance, and interactive task management, where accurately dis…

cs.LG2026

How to Allocate, How to Learn? Dynamic Rollout Allocation and Advantage Modulation for Policy Optimization

Yangyi Fang, Jiaye Lin, Xiaoliang Fu +6

Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for Large Language Model (LLM) reasoning, yet current methods face key challenges in resource allocation…

cs.LG2026

MASPO: Unifying Gradient Utilization, Probability Mass, and Signal Reliability for Robust and Sample-Efficient LLM Reasoning

Xiaoliang Fu, Jiaye Lin, Yangyi Fang +7

Existing Reinforcement Learning with Verifiable Rewards (RLVR) algorithms, such as GRPO, rely on rigid, uniform, and symmetric trust region mechanisms that are fundamentally misali…