Aligning language models to follow instructions
OpenAI published a blog post describing their work on aligning language models to follow human instructions, corresponding to the InstructGPT research. This work introduced reinforcement learning from human feedback (RLHF) as a core technique for training models to be more helpful, honest, and aligned with user intent. The approach demonstrated that smaller instruction-tuned models could outperform larger base models on human preference evaluations, marking a foundational shift in how language models are trained and deployed.
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Illustrating Reinforcement Learning from Human Feedback (RLHF)
This Hugging Face blog post provides an illustrated overview of Reinforcement Learning from Human Feedback (RLHF), explaining the technique used to align large language models with human preferences. It covers the core pipeline: pretraining a language model, collecting human preference data, training a reward model, and fine-tuning with RL. Published in December 2022, it served as an accessible reference during the period when RLHF was becoming central to frontier model development.
Learning to Summarize with Human Feedback
OpenAI published research applying reinforcement learning from human feedback (RLHF) to train language models for improved summarization quality. The work demonstrated that models trained with human preference signals outperform those trained purely on supervised objectives for summarization tasks. This paper is an early foundational contribution to the RLHF methodology that later became central to aligning large language models.
Improving language model behavior by training on a curated dataset
OpenAI published research showing that fine-tuning language models on a small, curated dataset can improve alignment with specific behavioral values. The work demonstrates a targeted approach to shaping model behavior without large-scale retraining. This represents an early contribution to what would become the RLHF and instruction-tuning research lineage.
Our approach to alignment research
OpenAI outlines its alignment research strategy, centered on improving AI systems' ability to learn from human feedback and to assist humans in evaluating AI outputs. The stated long-term goal is to build a sufficiently aligned AI system capable of helping solve remaining alignment problems. This represents OpenAI's public framing of its scalable oversight and RLHF-centric research agenda as of mid-2022.
RL-based alignment improves interactivity in full-duplex spoken dialogue models
Researchers propose a post-training alignment method using reinforcement learning to improve interactivity in full-duplex spoken dialogue models, which can listen and speak simultaneously. The method addresses four canonical axes of interactivity—pause handling, turn-taking, backchanneling, and user interruption—each with axis-specific reward functions, plus an LLM-based reward to prevent semantic degradation. The approach is applied to two open-source models, Moshi and PersonaPlex, showing consistent improvements in both offline and real-time multi-turn evaluation.
Why Language Models Hallucinate
OpenAI published research explaining the mechanisms behind language model hallucination. The work connects improved evaluation methods to enhanced AI reliability, honesty, and safety. The body is sparse on technical detail, but the framing positions this as foundational research relevant to alignment and deployment trust.
Unlocking Agentic RL Training for GPT-OSS: A Practical Retrospective
A Hugging Face blog post authored by LinkedIn describes practical lessons from implementing reinforcement learning training for agentic open-source GPT-class models. The retrospective covers engineering and algorithmic challenges encountered when applying RL to agentic workflows. As a tier-2 source with no body content available, the depth and specific findings cannot be fully assessed, but the topic sits at the intersection of agentic systems and RLHF/RL training pipelines.
Improving instruction hierarchy in frontier LLMs
OpenAI introduces IH-Challenge, a training approach designed to improve instruction hierarchy (IH) in large language models. The method trains models to correctly prioritize trusted instructions over untrusted ones, enhancing safety steerability and resistance to prompt injection attacks. This work addresses a core alignment challenge in deployed LLM systems where conflicting instructions from different principals must be handled reliably.