Towards Data Science September 23, 2026 By Benjamin Nweke

How GRPO Trains Small Language Models with Verifiable Rewards

Read the original on Towards Data Science →

The article explains how GRPO trains small language models using verifiable rewards, focusing on the mechanics of local reasoning experiments with Unsloth. It emphasizes that the reward function is as crucial as the model itself in shaping the training process. The post was originally published on Towards Data Science.

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arXiv Machine Learning

Aug 17

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By Konstantin Dobler, Federico Scozzafava, Jonathan Janke, Mohamed Ali, Simon Lehnerer

llms reinforcement-learning

arXiv AI

4d ago

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By Shangjian Yin, Yu Fu, Yue Dong, Zhouxing Shi

llms reinforcement-learning

arXiv AI

Oct 2

Exploring More, Reasoning Better: Stepwise Risk-Sensitive GRPO for Diffusion Language Models

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By Yue YU, Bowen Zuo, David Crandall, Yinglun Zhu, Dongruo Zhou

llms diffusion reinforcement-learning benchmarks

arXiv AI

Jul 15

A Learning-Rate-Gated Failure of GRPO in a Small Language and Vision-Language Model Web Agent: A Controlled Null and Its Mechanism

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By Chengguang Gan, Zhixi Cai, Yunhao Liang, Hanjun Wei, Shiwen Ni, Qinghao Zhang

llms rag agents reinforcement-learning multimodal

arXiv Machine Learning

Oct 8

BoT-GRPO: Efficient Process-Reward RL for Reasoning via Bag-of-Token Aggregation

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By Yingxiang Yang, Weihang Xiao, Zhunxuan Wang, Joshua Flashner, Niresh Agarwal

llms reinforcement-learning multimodal

arXiv AI

Sep 11

Proof-Carrying Cognition: Closing the Verification Gap with Reality-Settled Reward

The paper introduces the concept of proof‑carrying cognition, aiming to close the verification gap in language‑model reasoning by using reality‑settled rewards. It presents a theoretical framework linking verifier‑gold correlation to compute‑capability trade‑offs, demonstrates that unsound verifiers degrade under best‑of‑N selection while sound verifiers improve, and proposes a new benchmark metric, Soundness‑under‑Pressure, for evaluating reality‑settled reasoning systems.

By Eshwar Reddy M, Sourav Karmakar

llms reinforcement-learning benchmarks safety

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