Observed Signal · May 19, 2026 · Explainer Article · Source: DEV Community · Impact: 1/5 · Sentiment: Positive
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.
Informational explainer about LLM alignment techniques; useful background for AI/LLM practitioners but not a platform policy, product launch, or industry-shifting announcement.
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Key Takeaways & Evidence Grounding
- Published on DEV Community on 2026-05-19 by Rijul Rajesh.
- Aligning a pretrained model typically involves two stages: Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF).
- SFT trains on human-written prompt–response pairs using standard backpropagation to make models generate more helpful and polite replies.
- SFT datasets are much smaller than pretraining corpora and can lead to overfitting, reducing generalization to unseen prompts.
- The article positions RLHF as a way to improve model behavior without creating very large manual supervised datasets.
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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.
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.
Online Reinforcement Learning for LLMs
The article explains online reinforcement learning (RL) applied to large language models (LLMs). Unlike offline RL, online approaches incorporate real-time feedback from live user interactions, enabling continuous adaptation to distribution shifts. It describes RL mechanics for language models: partially observable state representation (user text, conversation history, system instructions, tool outputs), actions as high-dimensional token sequences, and the complexity of modeling feedback for long-form text. Reward models—derived from human feedback, automated verification, or learned evaluators—produce composite reward signals that guide policy optimization (e.g., Proximal Policy Optimization). The piece compares human-in-the-loop rewards (highly subjective but costly and inconsistent) with verifiable automated rewards (scalable and objective), and concludes production systems often combine multiple reward sources to balance trade-offs.
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