SignalReasoner evaluates reinforcement fine‑tuning methods for the Qwen2.5‑3B‑Base language model on graduate‑level signal processing math problems from the WirelessMATHBench‑XL benchmark. Two training paradigms are compared: direct reinforcement learning with verifiable rewards, and supervised chain‑of‑thought fine‑tuning followed by domain‑specific reinforcement learning. The study benchmarks three policy optimization algorithms—GRPO, GSPO, and GMPO—to determine if domain‑aware chain‑of‑thought initialization improves performance, achieving a best accuracy of 39.12% versus 12.37% for the untrained base model.
By Guozheng Sun
arXiv:2609.37119v1 Announce Type: cross
Abstract: Recent approaches to reinforcement learning (RL) post-training for large language models increasingly remove the critic to reduce training instabilit...
By Hongyang Li, Xiao Li, Caesar Wu, Said Mammar, Gr\'egoire Danoy, Pascal Bouvry
arXiv:2604.02341v2 Announce Type: replace-cross
Abstract: Mathematical reasoning in large language models has improved substantially with reinforcement learning using verifiable rewards, where final...
By Mohammad Rezaei, Jens Lehmann, Sahar Vahdati
arXiv:2606. 01281v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for enhancing the reasoning capabilities of large language models (LLMs).
By Yixiu Mao, Yun Qu, Qi Wang, Heming Zou, Xiangyang Ji
The paper introduces Gradient-Aligned Reward (GAR), a reinforcement learning technique that uses truncated backpropagation to generate a compact gradient vector for each rollout and compares it to an expert-anchor gradient via cosine similarity. This dense, reasoning-aware reward improves large language model chain-of-thought reasoning on math benchmarks and transfers to other tasks without domain‑specific data, while adding less than 9% computational overhead. GAR outperforms existing baselines such as GRPO on Qwen3-4B and Qwen3-8B models.
By Leqi Zheng, Jinbo Su, Fang Niu, Chaokun Wang, Weiping Wang, Jiajun Zhang, Shannan Yan, Jie Wu, Zhaolu Kang, Rong Fu, Hang Zhang
arXiv:2508. 10123v3 Announce Type: replace-cross Abstract: Advanced reasoning in LLMs on challenging domains like mathematical reasoning can be tackled using verifiable rewards based reinforced fine-tuning (ReFT).
By Maxime Heuillet, Yufei Cui, Boxing Chen, Audrey Durand, Prasanna Parthasarathi