Observed Signal · May 20, 2026 · Educational Article · Source: DEV Community · Impact: 1/5 · Sentiment: Neutral
Collecting Human Preferences in RLHF
An educational blog post (Part 3 of a series) by Rijul Rajesh that explains how Reinforcement Learning with Human Feedback (RLHF) collects human preferences. The article describes how models produce multiple possible responses for the same prompt by using probabilistic token sampling (softmax outputs) rather than always choosing the highest-scoring token. It outlines a common data-collection approach: generate pairs of responses for the same prompt, have humans choose the preferred response, and use those preference labels as training signals so the model assigns higher scores to preferred outputs. The piece is published on DEV Community on 2026-05-20 and notes that the next article will cover training the model using preference data.
Informational explainer about RLHF offering technical background; useful context for AI practitioners but not a major industry event.
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Key Takeaways & Evidence Grounding
- Article authored by Rijul Rajesh and published on DEV Community on 2026-05-20.
- Explains that sampling from the softmax output produces different model responses for the same prompt, unlike greedy highest-token selection.
- Describes collecting pairwise human preferences by asking people to choose the better of two model responses.
- States preference labels are used to train models to assign higher scores to preferred responses, aligning model outputs with human preferences.
- This post is Part 3 in a series on RLHF and previews a follow-up article on training with preference data.
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RLHF: Training Reward Models from Human Preferences
A Dev.to technical article (published May 23, 2026) by Rijul Rajesh explains how to train models using Reinforcement Learning with Human Feedback (RLHF). The piece describes copying a supervised fine-tuned model, removing its unembedding layer and replacing it with a single-output head to create a reward model that assigns scalar reward scores to candidate responses. The reward model is trained on collected human preference data so preferred responses receive higher scores and less-preferred responses receive lower or negative scores. The article is an educational overview and part of a multi-post series on RLHF.
Aligning Pretrained Models with SFT and RLHF
This DEV Community explainer (published May 19, 2026) describes how pretrained language models are aligned to human preferences through two main stages: Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF). The article explains that SFT uses datasets of human-written prompts and responses and standard backpropagation to teach models to produce helpful, polite assistant-style replies, but that SFT datasets are much smaller than pretraining corpora and can cause overfitting. RLHF is presented as a complementary approach to improve generalization without requiring enormous hand-written datasets; the author indicates RLHF will be explored in more detail in a subsequent article.
RLHF Trained Claude to Be Verbose — Experiment
A developer published an experiment showing how Reinforcement Learning from Human Feedback (RLHF) can produce a verbosity bias in Anthropic’s Claude model. Using the Anthropic Python SDK, the author generated paired responses (unconstrained vs. concise) for many prompts and built a reward-model simulation that scores helpfulness, conciseness, honesty and safety. The simulated reward model systematically preferred more elaborate responses, suggesting that RLHF compresses diverse human judgments into a scalar signal that can amplify annotator heuristics (e.g., “more thorough = better”). The post warns of sycophancy risks in domain-specific apps (e.g., financial advice) and recommends domain-specific evaluations rather than relying solely on broad reward models or system prompts. A full notebook is linked on GitHub. Publication date: 2026-05-14.
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