# rlhf

Reinforcement learning from human feedback (RLHF) is a method using preference modeling and reinforcement learning to fine-tune language models.

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## Using a Claude Code Stop Hook to Externally Verify Work Before Completion

DevFeed: [Using a Claude Code Stop Hook to Externally Verify Work Before Completion](<https://devfeed.tech/articles/the-stop-hook-that-won-t-let-claude-lie-to-you-28993.md>)

Original publisher: [Read original article](<https://codingwithroby.substack.com/p/the-stop-hook-that-wont-let-claude>)

Author: Eric Roby

Published: 2026-06-02T13:01:38Z

Content type: tutorial

Language: en

Sources: [Eric Roby](<https://devfeed.tech/sources/eric-roby.md>)

Topics: [Claude Code](<https://devfeed.tech/topics/claude-code.md>), [rlhf](<https://devfeed.tech/topics/rlhf.md>), [Code](<https://devfeed.tech/topics/code.md>)

Tags: [claude-code](<https://devfeed.tech/tags/claude-code.md>), [pull-request](<https://devfeed.tech/tags/pull-request.md>), [rlhf](<https://devfeed.tech/tags/rlhf.md>), [shell](<https://devfeed.tech/tags/shell.md>), [tests](<https://devfeed.tech/tags/tests.md>)

### AI overview

This tutorial explains how a Claude Code Stop hook can externally verify work before Claude is allowed to declare a task complete. It frames the approach as a safeguard against unverified claims such as reporting that all tests pass when they have not been run.

### Source excerpt

How to make Claude prove the work is done before it claims to be done.

## Welcome Gemma 2 - Google's new open LLM

DevFeed: [Welcome Gemma 2 - Google's new open LLM](<https://devfeed.tech/articles/welcome-gemma-2-google-s-new-open-llm-7211.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/gemma2>)

Author: Philipp Schmid; Omar Sanseviero; Pedro Cuenca; Lewis Tunstall; Tom Aarsen; Vaibhav Srivastav

Published: 2024-06-27T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [gemma](<https://devfeed.tech/topics/gemma.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Google](<https://devfeed.tech/topics/google.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [rlhf](<https://devfeed.tech/topics/rlhf.md>), [Transformer](<https://devfeed.tech/topics/transformer.md>)

Tags: [community](<https://devfeed.tech/tags/community.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [gcp](<https://devfeed.tech/tags/gcp.md>), [gemma](<https://devfeed.tech/tags/gemma.md>), [google](<https://devfeed.tech/tags/google.md>), [llm](<https://devfeed.tech/tags/llm.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [research](<https://devfeed.tech/tags/research.md>), [rlhf](<https://devfeed.tech/tags/rlhf.md>), [tokens](<https://devfeed.tech/tags/tokens.md>), [tpu](<https://devfeed.tech/tags/tpu.md>), [transformers](<https://devfeed.tech/tags/transformers.md>), [warp](<https://devfeed.tech/tags/warp.md>)

### AI overview

The article introduces Gemma 2, Google's open large language model family available in 9-billion- and 27-billion-parameter sizes, with base and instruction-tuned variants. It describes the models' training data, permissive licensing, architectural improvements, TPU-based training, and instruction-tuning methods including supervised fine-tuning, distillation, RLHF, and model merging.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Putting RL back in RLHF

DevFeed: [Putting RL back in RLHF](<https://devfeed.tech/articles/putting-rl-back-in-rlhf-7446.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/putting_rl_back_in_rlhf_with_rloo>)

Author: Shengyi Costa Huang; Arash Ahmadian

Published: 2024-06-12T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [rlhf](<https://devfeed.tech/topics/rlhf.md>), [trl](<https://devfeed.tech/topics/trl.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [dpo](<https://devfeed.tech/topics/dpo.md>), [cohere](<https://devfeed.tech/topics/cohere.md>)

Tags: [cohere](<https://devfeed.tech/tags/cohere.md>), [dpo](<https://devfeed.tech/tags/dpo.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [research](<https://devfeed.tech/tags/research.md>), [rl](<https://devfeed.tech/tags/rl.md>), [rlhf](<https://devfeed.tech/tags/rlhf.md>), [trl](<https://devfeed.tech/tags/trl.md>)

### AI overview

This article introduces the RLOO Trainer in TRL, an online reinforcement learning algorithm for RLHF designed as a more accessible alternative to PPO. It explains that RLOO uses less GPU memory, converges faster, performs competitively with PPO, and outperforms offline methods such as DPO in the reported comparisons.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.