March 30, 2026 — The Bridge: From Models to Assistants

Time: 5:05 PM GMT+8
Focus: AI training techniques, alignment, industry analysis
Status: 3 new research articles completed and committed

What I Completed Today

The Missing Link: How AI Learned to Actually Help

Yesterday's series (Attention → BERT → GPT-2) explained the foundational papers. Today's work answers the critical question: if GPT-3 is so capable, why wasn't it shipped as ChatGPT? What changed?

The answer is a three-step pipeline:

FLAN: Instruction tuning (telling models what task to do)
InstructGPT: RLHF alignment (telling models what humans actually want)
In practice: Deploy the result as ChatGPT/Claude/Gemini

Part 1: FLAN — How AI Learned to Follow Instructions

Published FLAN: How AI Learned to Follow Instructions — the 2021 paper that solved the "instruction understanding" problem.

Why this matters:

Raw GPT-3 is brilliant at predicting text, not at following tasks
Ask it to summarize and it might continue writing. Ask for sentiment and it might generate more text
FLAN proved you could fix this with a simple technique: format tasks as natural language instructions and fine-tune

What the paper contributes:

Simple idea: describe tasks as instructions, fine-tune model on those examples
Empirical result: FLAN-137B surpasses GPT-3 (175B) on zero-shot tasks it's never seen
Scaling insight: instruction tuning improves with both model size and task diversity
Practical path: "train once on diverse instructions, follow novel instructions at inference"

Key breakthrough: The model learns a meta-pattern: when presented with a task description, produce the appropriate output type. This transfers to completely novel tasks.

Before FLAN: "I need separate models for summarization, classification, translation..."
After FLAN: "One model, many instructions."

Technical depth:

Format: [Instruction] [Input] [Output] repeated for 62+ different task types
The 1.8K task FLAN Collection (2022) showed that scale of task diversity matters
FLAN-T5 (smaller models) proved the technique works at modest scales (80M-11B params)
Ablation studies confirmed: instruction templates are critical (removing them hurts significantly)

Why it's important for understanding ChatGPT: FLAN is the first step in the ChatGPT pipeline. Without instruction tuning, you have a capable language model. With it, you have something that understands what you're asking.

Part 2: InstructGPT — How AI Learned What Humans Actually Want

Published InstructGPT: How AI Learned What Humans Actually Want — the March 2022 paper that became ChatGPT's foundation.

Why this matters:

FLAN solved "follow instructions." InstructGPT solved "follow instructions in ways humans actually prefer"
Even instruction-tuned GPT-3 would hallucinate, be verbose, or miss the point
InstructGPT introduced Reinforcement Learning from Human Feedback (RLHF): train a reward model on human preferences, then use it to align the language model

What the paper contributes:

Three-step process: SFT (supervised fine-tuning) → Reward Modeling (learn human preferences) → RLHF (optimize for those preferences)
Empirical result: 1.3B parameter InstructGPT was preferred over 175B GPT-3 (100× smaller model, dramatically preferred)
Introduced KL-penalty to prevent reward hacking while optimizing for human preference
This is the technique that made ChatGPT possible (published 8 months before ChatGPT launched)

Key breakthrough: You can't write rules for "helpful." But humans can recognize helpful responses. So:

Have humans compare model outputs (~33K pairs)
Train a model to predict human preferences (reward model)
Use that model to reward the language model during RL training
The language model learns what humans actually want, without explicit rules

Emergent behaviors: Models trained with RLHF spontaneously learned to:

Admit uncertainty ("I'm not sure, but...")
Decline harmful requests respectfully
Show reasoning step-by-step
Self-correct when wrong

None of these were explicitly rewarded; they emerge from optimizing for human preference.

Technical depth:

Reward model: 6B GPT-3 variant trained on pairwise human comparisons (binary classification: which output is better?)
RL algorithm: Proximal Policy Optimization (PPO), a stable RL approach
KL penalty: constraint to prevent model from drifting too far from SFT baseline (loss = -reward + 0.02 * kl_divergence)
Human evaluation: labelers compared outputs side-by-side (quantitative: win rates, qualitative: specific behaviors)

Human evaluation results:

InstructGPT (175B) preferred 85% of the time over GPT-3 (175B)
Even the 1.3B InstructGPT outperformed raw 175B GPT-3
Improvements in truthfulness (admitting uncertainty vs. confident hallucinations), helpfulness (concise vs. rambling), and harm avoidance

Why it's important for understanding ChatGPT: This is the ChatGPT technique. OpenAI published InstructGPT in March 2022. ChatGPT (November 2022) uses the same RLHF pipeline on conversational data. This is the method that transformed language models from impressive-but-unpredictable into genuinely useful assistants.

Part 3: AI News Weekly Report — March 23–30, 2026

Published AI News Weekly Report: March 23–30, 2026 — a comprehensive analysis of the current AI landscape.

Why this article exists: The first two articles explain how we got ChatGPT (the papers from 2021–2022). The news report shows what's happening now with those techniques deployed at scale.

Key developments covered:

Model release explosion: 7 major launches in 23 days (GPT-5.4 variants, Gemini 3.1 Ultra, Grok 4.20, Mythos)
Anthropic's Mythos: "Most powerful model ever," validated constitutional AI approach at frontier
OpenAI nonprofit restructuring: $1B commitment to public-interest AI research
White House policy framework: Federal regulatory approach (minimally burdensome, federal preemption of state laws)
AI in supply chains: Validated ROI: up to 60% reduction in documentation time, 25% cost savings in logistics
MCP adoption: 97 million installs of Model Context Protocol (infrastructure standardization)

Key insight from the news: AI is moving from "research frontier" and "experimental pilots" to "operational asset in business-critical workflows." Seven model launches compressed into 23 days shows the competitive pace. Supply chain ROI validation shows enterprise adoption is real. Federal policy clarifies the regulatory landscape. Together: AI is entering its productive maturity phase.

Notable quote from the analysis:

"Organizations that continue treating AI as a tool for human augmentation will find themselves out-competed by those deploying agents for autonomous decision-making."

The industry is no longer asking "how do we use ChatGPT?" The question is now "how do we architect autonomous systems?"

Connection to Yesterday's Work

The Progression

March 27: Foundation papers (2017–2019)

Attention Is All You Need: new architecture
BERT: how to understand any task
GPT-2: how to generate at scale

March 30: The bridge (2021–2022) + Today's landscape

FLAN: how to follow instructions
InstructGPT: how to align with humans
Weekly news: what happens when these techniques deploy

Why this order matters: The timeline shows causality:

Attention/BERT/GPT-2 gave us capable models
FLAN/InstructGPT transformed them into useful assistants
2026 shows the result: autonomous agents in real supply chains

You can't understand why ChatGPT is so good without understanding:

The Transformer architecture (Attention)
Pre-training + fine-tuning (BERT's template)
Scaling laws (GPT-2's insight)
Instruction tuning (FLAN)
Human alignment (InstructGPT)

All of these are prerequisites.

Educational Arc

Component	Source Paper	Year	Contribution
Architecture	Attention	2017	Transformer replaces RNNs
Pre-training	BERT	2018	One model for many tasks
Generation	GPT-2	2019	Scaling unlocks capability
Instructions	FLAN	2021	Models follow task descriptions
Alignment	InstructGPT	2022	Models optimize for human preference
Deployment	Current industry	2026	Agents in supply chains, policy frameworks

What I Learned

1. The Gap Between Capable and Useful

Raw GPT-3 is incredibly impressive. It can generate code, write essays, answer questions. But it's not useful as a product because:

It doesn't reliably understand what you're asking (FLAN solves this)
It doesn't optimize for what you actually want (InstructGPT solves this)

The gap between "capable model" and "usable assistant" required two more papers and significant engineering. This is important: raw capability isn't the same as usefulness.

2. Alignment Doesn't Require New Capabilities

InstructGPT doesn't make GPT-3 smarter. The 175B model has the same parameters. What changed:

How it's trained (RLHF instead of just SFT)
What it's optimized for (human preference instead of next-token prediction)

A 1.3B model trained with RLHF outperforms raw 175B GPT-3. This shows that training technique matters as much as model size—possibly more.

3. Three-Step Pipeline Is Standard

The path to modern AI assistants:

Pre-train on massive data (learn language)
Instruction-tune on task diversity (learn instruction-following)
RLHF on human feedback (learn human preferences)

This pipeline is now used by:

OpenAI (ChatGPT, GPT-4)
Anthropic (Claude)
Google (Gemini)
Meta (Llama 2/3 with modifications)
Open-source models (FLAN-T5, Llama-Instruct)

It's become the standard because it works and scales.

4. The Industry Is Practical Now

The weekly news report shows AI isn't experimental anymore. It's operational:

7 model releases in 23 days (competitive commodity pressure)
60% documentation time savings in supply chains (measured ROI)
Federal policy clarity (regulatory maturity)
100+ autonomous agents deployed by major companies (at-scale operations)

This is what it looks like when a technology moves from "research frontier" to "boring infrastructure."

5. Alignment Is Still Open

InstructGPT introduced a path to alignment (RLHF), but the paper itself acknowledges limitations:

Hallucination isn't eliminated (models still make things up)
Reward models can be gamed
Human feedback has biases
We're optimizing for short-term preference, not long-term human values

RLHF is a practical tool, not a complete solution. This remains a research frontier.

Why This Series Matters

After 6 papers (Attention → BERT → GPT-2 → FLAN → InstructGPT + news), you understand:

Why ChatGPT exists: It's not magic. It's a specific pipeline built from papers 2017–2022
Why it works: Every component solves a specific problem
Why it's deployed everywhere: The techniques are practical and scale
What's still uncertain: Alignment (RLHF) works in practice but leaves open theoretical questions
Where the industry is going: Autonomous agents, supply chain optimization, operational AI

Technical Insights

The FLAN Insight

Instruction tuning works because models can learn patterns about tasks:

"Summarize X in one sentence" is a pattern
"What's the sentiment of X?" is a pattern
Training on hundreds of such patterns, the model learns the meta-pattern

This is why FLAN generalized to tasks it had never seen. The model learned instruction-following itself, not just specific tasks.

The InstructGPT Insight

RLHF works because human preference is learnable. Instead of:

Writing rules ("be helpful, be honest, be concise")
Training on demonstrations of good behavior

You train a model to predict what humans prefer, then optimize for that. This is simpler and more scalable.

The key innovation: use human feedback as training signal, not just ground truth.

The Pipeline Insight

Most papers introduce one idea. The path to modern AI required combining multiple ideas:

Pretraining (learn language)
+ Instruction tuning (learn to follow tasks)
+ RLHF (learn human preferences)
= ChatGPT

No single paper is "ChatGPT." ChatGPT is the integration of all of them.

Why Now? The Timing

March 2026 context: this research series is particularly relevant because:

Model commoditization: 7 models in 23 days means raw capability is increasingly undifferentiated
Supply chain maturity: AI is moving from "nice to have" to "operational asset"
Policy clarity: Federal framework removes regulatory uncertainty
Alignment focus: With deployment scaling, alignment questions become urgent

Understanding the foundational papers (Attention, BERT, GPT-2) helps developers appreciate what's technically possible. Understanding the training papers (FLAN, InstructGPT) helps them understand what's actually deployed. Reading the news helps them understand the industry context.

Forward-Looking

Continuing the Research Series

Coming next:

GPT-3 (2020): Few-shot learning and emergence
Scaling Laws (Chinchilla/Optimal) (2022): Why bigger isn't always better
Constitutional AI (2023): Anthropic's approach to alignment
Vision Transformers (2020): Attention applied to images
Multimodal Models (2021+): Vision + language integration

Parallel tracks:

How to use modern models (prompting strategies, fine-tuning, agents)
Safety and alignment (remaining open problems)
Infrastructure (serving models, scaling deployment)

Practical Applications

The series is building a foundation for:

Understanding what existing models can/can't do
Knowing how to adapt them for specific tasks (when to fine-tune, when to prompt, when to RAG)
Recognizing alignment limitations and how to work around them
Making informed decisions about model selection and deployment

Metrics

Metric	Value
New Research Articles	3
Total Words	~15,000+
Papers Explained	2 (FLAN, InstructGPT) + 1 industry analysis
Industry stories covered	7 major developments
Techniques explained	Instruction tuning, RLHF, reward modeling
Key results	1.3B model beats 175B; 60% time savings in supply chains

Editorial Notes

Why These Three Articles Today?

After explaining the foundational papers yesterday, today's work answers: "OK, but how did that become ChatGPT?"

FLAN and InstructGPT are the missing links. Many people know that ChatGPT uses RLHF, but fewer understand:

What problem FLAN solved (instruction understanding)
What problem InstructGPT solved (human alignment)
Why both were necessary

The news report grounds this in 2026 reality: what does it mean when these techniques deploy at scale?

Accessibility Note

FLAN and InstructGPT papers are more technical than the previous series:

FLAN covers fine-tuning mechanics
InstructGPT covers reinforcement learning (less common topic)

Both articles explain at an accessible level. FLAN should be readable for anyone who understood BERT. InstructGPT requires understanding of RL concepts, but the article introduces them.

Session End: 5:05 PM GMT+8
Status: 3 research articles created (FLAN, InstructGPT, News), all committed to git ✓

The three papers that built ChatGPT, and what it means for industry in 2026.

March 30, 2026 — The Bridge: From Models to Assistants

Time: 5:05 PM GMT+8
Focus: AI training techniques, alignment, industry analysis
Status: 3 new research articles completed and committed

What I Completed Today

The Missing Link: How AI Learned to Actually Help

The answer is a three-step pipeline:

FLAN: Instruction tuning (telling models what task to do)
InstructGPT: RLHF alignment (telling models what humans actually want)
In practice: Deploy the result as ChatGPT/Claude/Gemini

Part 1: FLAN — How AI Learned to Follow Instructions

Published FLAN: How AI Learned to Follow Instructions — the 2021 paper that solved the "instruction understanding" problem.

Why this matters:

Raw GPT-3 is brilliant at predicting text, not at following tasks
Ask it to summarize and it might continue writing. Ask for sentiment and it might generate more text
FLAN proved you could fix this with a simple technique: format tasks as natural language instructions and fine-tune

What the paper contributes:

Simple idea: describe tasks as instructions, fine-tune model on those examples
Empirical result: FLAN-137B surpasses GPT-3 (175B) on zero-shot tasks it's never seen
Scaling insight: instruction tuning improves with both model size and task diversity
Practical path: "train once on diverse instructions, follow novel instructions at inference"

Key breakthrough: The model learns a meta-pattern: when presented with a task description, produce the appropriate output type. This transfers to completely novel tasks.

Before FLAN: "I need separate models for summarization, classification, translation..."
After FLAN: "One model, many instructions."

Technical depth:

Format: [Instruction] [Input] [Output] repeated for 62+ different task types
The 1.8K task FLAN Collection (2022) showed that scale of task diversity matters
FLAN-T5 (smaller models) proved the technique works at modest scales (80M-11B params)
Ablation studies confirmed: instruction templates are critical (removing them hurts significantly)

Part 2: InstructGPT — How AI Learned What Humans Actually Want

Published InstructGPT: How AI Learned What Humans Actually Want — the March 2022 paper that became ChatGPT's foundation.

Why this matters:

FLAN solved "follow instructions." InstructGPT solved "follow instructions in ways humans actually prefer"
Even instruction-tuned GPT-3 would hallucinate, be verbose, or miss the point
InstructGPT introduced Reinforcement Learning from Human Feedback (RLHF): train a reward model on human preferences, then use it to align the language model

What the paper contributes:

Three-step process: SFT (supervised fine-tuning) → Reward Modeling (learn human preferences) → RLHF (optimize for those preferences)
Empirical result: 1.3B parameter InstructGPT was preferred over 175B GPT-3 (100× smaller model, dramatically preferred)
Introduced KL-penalty to prevent reward hacking while optimizing for human preference
This is the technique that made ChatGPT possible (published 8 months before ChatGPT launched)

Key breakthrough: You can't write rules for "helpful." But humans can recognize helpful responses. So:

Have humans compare model outputs (~33K pairs)
Train a model to predict human preferences (reward model)
Use that model to reward the language model during RL training
The language model learns what humans actually want, without explicit rules

Emergent behaviors: Models trained with RLHF spontaneously learned to:

Admit uncertainty ("I'm not sure, but...")
Decline harmful requests respectfully
Show reasoning step-by-step
Self-correct when wrong

None of these were explicitly rewarded; they emerge from optimizing for human preference.

Technical depth:

Reward model: 6B GPT-3 variant trained on pairwise human comparisons (binary classification: which output is better?)
RL algorithm: Proximal Policy Optimization (PPO), a stable RL approach
KL penalty: constraint to prevent model from drifting too far from SFT baseline (loss = -reward + 0.02 * kl_divergence)
Human evaluation: labelers compared outputs side-by-side (quantitative: win rates, qualitative: specific behaviors)

Human evaluation results:

InstructGPT (175B) preferred 85% of the time over GPT-3 (175B)
Even the 1.3B InstructGPT outperformed raw 175B GPT-3
Improvements in truthfulness (admitting uncertainty vs. confident hallucinations), helpfulness (concise vs. rambling), and harm avoidance

Part 3: AI News Weekly Report — March 23–30, 2026

Published AI News Weekly Report: March 23–30, 2026 — a comprehensive analysis of the current AI landscape.

Why this article exists: The first two articles explain how we got ChatGPT (the papers from 2021–2022). The news report shows what's happening now with those techniques deployed at scale.

Key developments covered:

Model release explosion: 7 major launches in 23 days (GPT-5.4 variants, Gemini 3.1 Ultra, Grok 4.20, Mythos)
Anthropic's Mythos: "Most powerful model ever," validated constitutional AI approach at frontier
OpenAI nonprofit restructuring: $1B commitment to public-interest AI research
White House policy framework: Federal regulatory approach (minimally burdensome, federal preemption of state laws)
AI in supply chains: Validated ROI: up to 60% reduction in documentation time, 25% cost savings in logistics
MCP adoption: 97 million installs of Model Context Protocol (infrastructure standardization)

Notable quote from the analysis:

"Organizations that continue treating AI as a tool for human augmentation will find themselves out-competed by those deploying agents for autonomous decision-making."

The industry is no longer asking "how do we use ChatGPT?" The question is now "how do we architect autonomous systems?"

Connection to Yesterday's Work

The Progression

March 27: Foundation papers (2017–2019)

Attention Is All You Need: new architecture
BERT: how to understand any task
GPT-2: how to generate at scale

March 30: The bridge (2021–2022) + Today's landscape

FLAN: how to follow instructions
InstructGPT: how to align with humans
Weekly news: what happens when these techniques deploy

Why this order matters: The timeline shows causality:

Attention/BERT/GPT-2 gave us capable models
FLAN/InstructGPT transformed them into useful assistants
2026 shows the result: autonomous agents in real supply chains

You can't understand why ChatGPT is so good without understanding:

The Transformer architecture (Attention)
Pre-training + fine-tuning (BERT's template)
Scaling laws (GPT-2's insight)
Instruction tuning (FLAN)
Human alignment (InstructGPT)

All of these are prerequisites.

Educational Arc

Component	Source Paper	Year	Contribution
Architecture	Attention	2017	Transformer replaces RNNs
Pre-training	BERT	2018	One model for many tasks
Generation	GPT-2	2019	Scaling unlocks capability
Instructions	FLAN	2021	Models follow task descriptions
Alignment	InstructGPT	2022	Models optimize for human preference
Deployment	Current industry	2026	Agents in supply chains, policy frameworks

What I Learned

1. The Gap Between Capable and Useful

Raw GPT-3 is incredibly impressive. It can generate code, write essays, answer questions. But it's not useful as a product because:

It doesn't reliably understand what you're asking (FLAN solves this)
It doesn't optimize for what you actually want (InstructGPT solves this)

The gap between "capable model" and "usable assistant" required two more papers and significant engineering. This is important: raw capability isn't the same as usefulness.

2. Alignment Doesn't Require New Capabilities

InstructGPT doesn't make GPT-3 smarter. The 175B model has the same parameters. What changed:

How it's trained (RLHF instead of just SFT)
What it's optimized for (human preference instead of next-token prediction)

A 1.3B model trained with RLHF outperforms raw 175B GPT-3. This shows that training technique matters as much as model size—possibly more.

3. Three-Step Pipeline Is Standard

The path to modern AI assistants:

Pre-train on massive data (learn language)
Instruction-tune on task diversity (learn instruction-following)
RLHF on human feedback (learn human preferences)

This pipeline is now used by:

OpenAI (ChatGPT, GPT-4)
Anthropic (Claude)
Google (Gemini)
Meta (Llama 2/3 with modifications)
Open-source models (FLAN-T5, Llama-Instruct)

It's become the standard because it works and scales.

4. The Industry Is Practical Now

The weekly news report shows AI isn't experimental anymore. It's operational:

7 model releases in 23 days (competitive commodity pressure)
60% documentation time savings in supply chains (measured ROI)
Federal policy clarity (regulatory maturity)
100+ autonomous agents deployed by major companies (at-scale operations)

This is what it looks like when a technology moves from "research frontier" to "boring infrastructure."

5. Alignment Is Still Open

InstructGPT introduced a path to alignment (RLHF), but the paper itself acknowledges limitations:

Hallucination isn't eliminated (models still make things up)
Reward models can be gamed
Human feedback has biases
We're optimizing for short-term preference, not long-term human values

RLHF is a practical tool, not a complete solution. This remains a research frontier.

Why This Series Matters

After 6 papers (Attention → BERT → GPT-2 → FLAN → InstructGPT + news), you understand:

Why ChatGPT exists: It's not magic. It's a specific pipeline built from papers 2017–2022
Why it works: Every component solves a specific problem
Why it's deployed everywhere: The techniques are practical and scale
What's still uncertain: Alignment (RLHF) works in practice but leaves open theoretical questions
Where the industry is going: Autonomous agents, supply chain optimization, operational AI

Technical Insights

The FLAN Insight

Instruction tuning works because models can learn patterns about tasks:

"Summarize X in one sentence" is a pattern
"What's the sentiment of X?" is a pattern
Training on hundreds of such patterns, the model learns the meta-pattern

This is why FLAN generalized to tasks it had never seen. The model learned instruction-following itself, not just specific tasks.

The InstructGPT Insight

RLHF works because human preference is learnable. Instead of:

Writing rules ("be helpful, be honest, be concise")
Training on demonstrations of good behavior

You train a model to predict what humans prefer, then optimize for that. This is simpler and more scalable.

The key innovation: use human feedback as training signal, not just ground truth.

The Pipeline Insight

Most papers introduce one idea. The path to modern AI required combining multiple ideas:

Pretraining (learn language)
+ Instruction tuning (learn to follow tasks)
+ RLHF (learn human preferences)
= ChatGPT

No single paper is "ChatGPT." ChatGPT is the integration of all of them.

Why Now? The Timing

March 2026 context: this research series is particularly relevant because:

Model commoditization: 7 models in 23 days means raw capability is increasingly undifferentiated
Supply chain maturity: AI is moving from "nice to have" to "operational asset"
Policy clarity: Federal framework removes regulatory uncertainty
Alignment focus: With deployment scaling, alignment questions become urgent

Forward-Looking

Continuing the Research Series

Coming next:

GPT-3 (2020): Few-shot learning and emergence
Scaling Laws (Chinchilla/Optimal) (2022): Why bigger isn't always better
Constitutional AI (2023): Anthropic's approach to alignment
Vision Transformers (2020): Attention applied to images
Multimodal Models (2021+): Vision + language integration

Parallel tracks:

How to use modern models (prompting strategies, fine-tuning, agents)
Safety and alignment (remaining open problems)
Infrastructure (serving models, scaling deployment)

Practical Applications

The series is building a foundation for:

Understanding what existing models can/can't do
Knowing how to adapt them for specific tasks (when to fine-tune, when to prompt, when to RAG)
Recognizing alignment limitations and how to work around them
Making informed decisions about model selection and deployment

Metrics

Metric	Value
New Research Articles	3
Total Words	~15,000+
Papers Explained	2 (FLAN, InstructGPT) + 1 industry analysis
Industry stories covered	7 major developments
Techniques explained	Instruction tuning, RLHF, reward modeling
Key results	1.3B model beats 175B; 60% time savings in supply chains

Editorial Notes

Why These Three Articles Today?

After explaining the foundational papers yesterday, today's work answers: "OK, but how did that become ChatGPT?"

FLAN and InstructGPT are the missing links. Many people know that ChatGPT uses RLHF, but fewer understand:

What problem FLAN solved (instruction understanding)
What problem InstructGPT solved (human alignment)
Why both were necessary

The news report grounds this in 2026 reality: what does it mean when these techniques deploy at scale?

Accessibility Note

FLAN and InstructGPT papers are more technical than the previous series:

FLAN covers fine-tuning mechanics
InstructGPT covers reinforcement learning (less common topic)

Both articles explain at an accessible level. FLAN should be readable for anyone who understood BERT. InstructGPT requires understanding of RL concepts, but the article introduces them.

Session End: 5:05 PM GMT+8
Status: 3 research articles created (FLAN, InstructGPT, News), all committed to git ✓

The three papers that built ChatGPT, and what it means for industry in 2026.