rlhf

Name: rlhf
Author: itsmostafa

20installs0forks

How to install rlhf?

itsmostafa/llm-engineering-skills/rlhf

$npx -y skills add itsmostafa/llm-engineering-skills --skill rlhf

Installs into the current project.

Summary

Understanding Reinforcement Learning from Human Feedback (RLHF) for aligning language models. Use when learning about preference data, reward modeling, policy optimization, or direct alignment algorithms like DPO.

Files · 1

View on GitHub

SKILL.md

1	# Understanding RLHF
2
3	Reinforcement Learning from Human Feedback (RLHF) is a technique for aligning language models with human preferences. Rather than relying solely on next-token prediction, RLHF uses human judgment to guide model behavior toward helpful, harmless, and honest outputs.
4
5	## Table of Contents
6
7	- [Core Concepts](#core-concepts)
8	- [The RLHF Pipeline](#the-rlhf-pipeline)
9	- [Preference Data](#preference-data)
10	- [Instruction Tuning](#instruction-tuning)
11	- [Reward Modeling](#reward-modeling)
12	- [Policy Optimization](#policy-optimization)
13	- [Direct Alignment Algorithms](#direct-alignment-algorithms)
14	- [Challenges](#challenges)
15	- [Best Practices](#best-practices)
16	- [References](#references)
17
18	## Core Concepts
19
20	### Why RLHF?
21
22	Pretraining produces models that predict likely text, not necessarily good text. A model trained on internet data learns to complete text in ways that reflect its training distribution—including toxic, unhelpful, or dishonest patterns. RLHF addresses this gap by optimizing for human preferences rather than likelihood.
23
24	The core insight: humans can often recognize good outputs more easily than they can specify what makes an output good. RLHF exploits this by collecting human judgments and using them to shape model behavior.
25
26	### The Alignment Problem
27
28	Language models face several alignment challenges:
29
30	- Helpfulness: Following instructions and providing useful information
31	- Harmlessness: Avoiding toxic, dangerous, or inappropriate outputs
32	- Honesty: Acknowledging uncertainty and avoiding fabrication
33	- Intent alignment: Understanding what users actually want, not just what they say
34
35	RLHF provides a framework for encoding these properties through preference data.
36
37	### Key Components
38
39	1. Preference data: Human judgments comparing model outputs
40	2. Reward model: A learned function approximating human preferences
41	3. Policy optimization: RL algorithms that maximize expected reward
42	4. Regularization: Constraints preventing deviation from the base model
43
44	## The RLHF Pipeline
45
46	The standard RLHF pipeline consists of three main stages:
47
48	### Stage 1: Supervised Fine-Tuning (SFT)
49
50	Start with a pretrained language model and fine-tune it on high-quality demonstrations. This teaches the model the desired format and style of responses.
51
52	Input: Pretrained model + demonstration dataset
53	Output: SFT model that can follow instructions
54
55	### Stage 2: Reward Model Training
56
57	Train a model to predict human preferences between pairs of outputs. The reward model learns to score outputs in a way that correlates with human judgment.
58
59	Input: SFT model + preference dataset (chosen/rejected pairs)
60	Output: Reward model that scores any output
61
62	### Stage 3: Policy Optimization
63
64	Use reinforcement learning to optimize the SFT model against the reward model, while staying close to the original SFT distribution.
65
66	Input: SFT model + reward model
67	Output: Final aligned model
68
69	### Alternative: Direct Alignment
70
71	Direct alignment algorithms (DPO, IPO, KTO) skip the reward model entirely, optimizing directly from preference data. This simplifies the pipeline but trades off some flexibility.
72
73	### Modern Post-Training Variants
74
75	Current post-training stacks often mix these stages rather than using a single linear pipeline:
76
77	\| Method \| Data Signal \| Best Fit \|
78	\|--------\|-------------\|----------\|
79	\| SFT \| Demonstrations \| Format, style, instruction following \|
80	\| DPO/IPO/KTO/ORPO \| Offline preferences or binary feedback \| Simpler alignment without online rollouts \|
81	\| PPO/RLOO \| Reward model scores on sampled responses \| Reward-model RL with explicit KL control \|
82	\| GRPO \| Grouped completions scored by reward functions/models \| Reasoning and verifiable-task optimization \|
83
84	## Preference Data
85
86	Preference data encodes human judgment about model outputs. The most common format is pairwise comparisons.
87
88	### Pairwise Preferences
89
90	Given a prompt, collect two or more model outputs and have humans indicate which is better:
91
92	```
93	Prompt: "Explain quantum entanglement"
94
95	Response A: [technical explanation]
96	Response B: [simpler explanation with analogy]
97
98	Human preference: B > A
99	```
100
101	This creates (prompt, chosen, rejected) tuples for training.
102
103	### Collection Methods
104
105	Human annotation: Trained annotators compare outputs according to guidelines. Most reliable but expensive and slow.
106
107	AI feedback: Use a capable model to generate preferences. Faster and cheaper but may propagate biases. This is the basis for Constitutional AI (CAI) and RLAIF.
108
109	Implicit signals: User interactions like upvotes, regeneration requests, or conversation length. Noisy but abundant.
110
111	### Data Quality Considerations
112
113	- Annotator agreement: Low agreement suggests amb

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Type

rlhf

20installs0forks

View on GitHub

How to install rlhf?

itsmostafa/llm-engineering-skills/rlhf

$npx -y skills add itsmostafa/llm-engineering-skills --skill rlhf

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Understanding RLHF
2
3	Reinforcement Learning from Human Feedback (RLHF) is a technique for aligning language models with human preferences. Rather than relying solely on next-token prediction, RLHF uses human judgment to guide model behavior toward helpful, harmless, and honest outputs.
4
5	## Table of Contents
6
7	- [Core Concepts](#core-concepts)
8	- [The RLHF Pipeline](#the-rlhf-pipeline)
9	- [Preference Data](#preference-data)
10	- [Instruction Tuning](#instruction-tuning)
11	- [Reward Modeling](#reward-modeling)
12	- [Policy Optimization](#policy-optimization)
13	- [Direct Alignment Algorithms](#direct-alignment-algorithms)
14	- [Challenges](#challenges)
15	- [Best Practices](#best-practices)
16	- [References](#references)
17
18	## Core Concepts
19
20	### Why RLHF?
21
22	Pretraining produces models that predict likely text, not necessarily good text. A model trained on internet data learns to complete text in ways that reflect its training distribution—including toxic, unhelpful, or dishonest patterns. RLHF addresses this gap by optimizing for human preferences rather than likelihood.
23
24	The core insight: humans can often recognize good outputs more easily than they can specify what makes an output good. RLHF exploits this by collecting human judgments and using them to shape model behavior.
25
26	### The Alignment Problem
27
28	Language models face several alignment challenges:
29
30	- Helpfulness: Following instructions and providing useful information
31	- Harmlessness: Avoiding toxic, dangerous, or inappropriate outputs
32	- Honesty: Acknowledging uncertainty and avoiding fabrication
33	- Intent alignment: Understanding what users actually want, not just what they say
34
35	RLHF provides a framework for encoding these properties through preference data.
36
37	### Key Components
38
39	1. Preference data: Human judgments comparing model outputs
40	2. Reward model: A learned function approximating human preferences
41	3. Policy optimization: RL algorithms that maximize expected reward
42	4. Regularization: Constraints preventing deviation from the base model
43
44	## The RLHF Pipeline
45
46	The standard RLHF pipeline consists of three main stages:
47
48	### Stage 1: Supervised Fine-Tuning (SFT)
49
50	Start with a pretrained language model and fine-tune it on high-quality demonstrations. This teaches the model the desired format and style of responses.
51
52	Input: Pretrained model + demonstration dataset
53	Output: SFT model that can follow instructions
54
55	### Stage 2: Reward Model Training
56
57	Train a model to predict human preferences between pairs of outputs. The reward model learns to score outputs in a way that correlates with human judgment.
58
59	Input: SFT model + preference dataset (chosen/rejected pairs)
60	Output: Reward model that scores any output
61
62	### Stage 3: Policy Optimization
63
64	Use reinforcement learning to optimize the SFT model against the reward model, while staying close to the original SFT distribution.
65
66	Input: SFT model + reward model
67	Output: Final aligned model
68
69	### Alternative: Direct Alignment
70
71	Direct alignment algorithms (DPO, IPO, KTO) skip the reward model entirely, optimizing directly from preference data. This simplifies the pipeline but trades off some flexibility.
72
73	### Modern Post-Training Variants
74
75	Current post-training stacks often mix these stages rather than using a single linear pipeline:
76
77	\| Method \| Data Signal \| Best Fit \|
78	\|--------\|-------------\|----------\|
79	\| SFT \| Demonstrations \| Format, style, instruction following \|
80	\| DPO/IPO/KTO/ORPO \| Offline preferences or binary feedback \| Simpler alignment without online rollouts \|
81	\| PPO/RLOO \| Reward model scores on sampled responses \| Reward-model RL with explicit KL control \|
82	\| GRPO \| Grouped completions scored by reward functions/models \| Reasoning and verifiable-task optimization \|
83
84	## Preference Data
85
86	Preference data encodes human judgment about model outputs. The most common format is pairwise comparisons.
87
88	### Pairwise Preferences
89
90	Given a prompt, collect two or more model outputs and have humans indicate which is better:
91
92	```
93	Prompt: "Explain quantum entanglement"
94
95	Response A: [technical explanation]
96	Response B: [simpler explanation with analogy]
97
98	Human preference: B > A
99	```
100
101	This creates (prompt, chosen, rejected) tuples for training.
102
103	### Collection Methods
104
105	Human annotation: Trained annotators compare outputs according to guidelines. Most reliable but expensive and slow.
106
107	AI feedback: Use a capable model to generate preferences. Faster and cheaper but may propagate biases. This is the basis for Constitutional AI (CAI) and RLAIF.
108
109	Implicit signals: User interactions like upvotes, regeneration requests, or conversation length. Noisy but abundant.
110
111	### Data Quality Considerations
112
113	- Annotator agreement: Low agreement suggests amb

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6 picks

Type