arXiv AI August 12, 2026 By Alec Harris, Kasey Corra, Archie Chaudhury, Yixiong Hao

Evaluation-Conditioned Training: Teaching Models to Generalize to Stronger Oversight Regimes

arXiv:2608. 10209v1 Announce Type: new Abstract: Feedback signals used to train Large Language Models (LLMs) are the primary driver of their behavior and our main lever for instilling alignment with human values and objectives.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Computation and Language

Sep 1

Small Reward Models via Backward Inference

arXiv:2602.13551v3 Announce Type: replace Abstract: Reward models (RMs) play a central role throughout the language model (LM) pipeline, particularly in non-verifiable domains. However, the dominant...

By Yike Wang, Faeze Brahman, Shangbin Feng, Teng Xiao, Hannaneh Hajishirzi, Yulia Tsvetkov

llms reinforcement-learning

arXiv AI

Jun 30

Distributionally Robust Reinforcement Learning with Human Feedback

arXiv:2503. 00539v2 Announce Type: replace-cross Abstract: Reinforcement learning from human feedback (RLHF) has evolved to be one of the main methods for fine-tuning large language models (LLMs).

By Debmalya Mandal, Paulius Sasnauskas, Goran Radanovic

llms reinforcement-learning fine-tuning

arXiv AI

Jul 2

Right in the Right Way: LM Training with Verifiable Rewards and Human Demonstrations

arXiv:2607. 01181v1 Announce Type: cross Abstract: RL with verifiable rewards (RLVR) has emerged as a powerful paradigm for training LMs on tasks with well-defined success metrics, such as code generation and mathematical reasoning.

By Mehul Damani, Isha Puri, Idan Shenfeld, Jacob Andreas

reinforcement-learning fine-tuning benchmarks safety

arXiv Computation and Language

Sep 22

Team DArgk at the 2026 ELOQUENT lab for evaluating generative language model quality: Residuals of Humanity: AI Detection Evasion via GRPO Fine-Tuning

arXiv:2609.22221v1 Announce Type: new Abstract: Large language models (LLMs) can generate fluent and coherent text that is increasingly difficult to distinguish from human writing, motivating the dev...

By Antonela Tommasel, Juan Manuel Rodriguez

llms reinforcement-learning fine-tuning safety

arXiv AI

Jul 7

Gradient Regularization Mitigates Reward Hacking in Reinforcement Learning from Human Feedback and Verifiable Rewards

arXiv:2602. 18037v2 Announce Type: replace-cross Abstract: Reinforcement Learning from Human Feedback (RLHF) or Verifiable Rewards (RLVR) are two key steps in the post-training of modern Language Models (LMs).

By Johannes Ackermann, Michael Noukhovitch, Takashi Ishida, Masashi Sugiyama

llms reinforcement-learning safety

arXiv Machine Learning

Jun 3

Skill-RM: Unifying Heterogeneous Evaluation Criteria via Agent Skill

arXiv:2606. 03980v1 Announce Type: new Abstract: Reward models (RMs) provide critical feedback signals for LLM post-training, notably in reinforced fine-tuning (RFT) and reinforcement learning (RL) pipelines.

By Tao Chen, Gangwei Jiang, Pengyu Cheng, Siyuan Huang, Yihao Liu, Jingwei Ni, Jiaqi Guo, Mengyu Zhou, Kai Tang, Junling Liu, Qinliang Su, Xiaoxi Jiang, Guanjun Jiang

llms agents reinforcement-learning fine-tuning benchmarks

One email a morning, machine-written

Related stories

Small Reward Models via Backward Inference

Distributionally Robust Reinforcement Learning with Human Feedback

Right in the Right Way: LM Training with Verifiable Rewards and Human Demonstrations

Team DArgk at the 2026 ELOQUENT lab for evaluating generative language model quality: Residuals of Humanity: AI Detection Evasion via GRPO Fine-Tuning

Gradient Regularization Mitigates Reward Hacking in Reinforcement Learning from Human Feedback and Verifiable Rewards

Skill-RM: Unifying Heterogeneous Evaluation Criteria via Agent Skill