Llama 2 and Llama 3 Analysis
Meta has released Llama 2 (model and finetune tutorial) recently, and one of my research works is possible to leverage its text completion capability for synthetic data generation.Llama 2: Open Foundation and Fine-Tuned Chat Models, I'd like to draft this blog to understand more details under the scene.
Llama 2 vs Llama 3 — What Actually Changed?
Meta released Llama 2 in 2023 and Llama 3 in 2024 as open-weight foundation models designed to compete with proprietary systems while remaining deployable by researchers.
Official page: https://ai.meta.com/llama/
For many research workflows — especially synthetic data generation, tool-augmented agents, and RL training pipelines — the difference between these two models is much deeper than a typical version upgrade.
This post explains what changed under the hood.
Quick Summary
| Aspect | Llama 2 | Llama 3 |
|---|---|---|
| Training philosophy | scaled GPT-style | reasoning-oriented scaling |
| Tokenizer | SentencePiece 32k | new tokenizer ~128k |
| Context length | 4k | 8k+ (much stronger long reasoning) |
| Data | filtered web + curated sources | heavily cleaned + code + synthetic |
| Alignment | RLHF tuned chat | instruction-following + reasoning alignment |
| Weakness | hallucination, brittle reasoning | much stronger logic consistency |
| Strength | controllable generation | robust multi-step reasoning |
Model Architecture Differences
Llama 2: Standard Scaling Era
Llama 2 follows the classical paradigm:
$$
Better model=more parameters + more tokens
$$
Improvements came mainly from:
- more training data
- longer training
- RLHF chat tuning
The architecture itself stayed conservative:
- decoder-only transformer
- RoPE positional encoding
- 4k context
- 32k tokenizer
So performance gains were primarily statistical rather than cognitive.
Llama 3: Post-ChatGPT Reasoning Era
Llama 3 changes philosophy:
$$
Better model=better distribution + better reasoning
$$
Meta shifted focus from language modeling to capability modeling.
Key architectural-level changes:
- much larger tokenizer (~128k tokens)
- improved attention stability
- training optimized for multi-step reasoning
- synthetic reasoning data
- long-context robustness
The result is not just higher benchmark scores — the model behaves differently:
Llama 2 predicts text
Llama 3 performs tasks
Tokenization — The Hidden Giant Upgrade
This is one of the most important but least discussed changes.
| Llama 2 | Llama 3 | |
|---|---|---|
| Vocabulary | 32k | ~128k |
| Effect | fragmentation | semantic tokens |
| Outcome | brittle reasoning | stable reasoning |
Why this matters:
Reasoning errors often come from token fragmentation:
“12345” → [12][345] (Llama 2)
“12345” → [12345] (Llama 3)
Llama 3 dramatically reduces token boundary mistakes, which improves:
- math
- code
- planning
- tool calls
This alone explains a large portion of the reasoning improvement.
Training Data Differences
Llama 2 Data
Mostly:
- filtered web corpus
- books
- code subset
- human preference tuning
Goal:
general helpful chat model
Llama 3 Data
Meta publicly described a major shift:
- aggressive deduplication
- higher-quality filtering
- synthetic reasoning data
- more code
- less noisy web text
Goal:
reliable decision-making model
This changes failure mode:
| Model | Failure |
|---|---|
| Llama 2 | fluent nonsense |
| Llama 3 | structured but incomplete reasoning |
Alignment Strategy
Llama 2 Alignment (RLHF-centric)
The chat model is optimized for human preference:
helpfulness>correctness\text{helpfulness} > \text{correctness}helpfulness>correctness
Typical behavior:
- polite
- verbose
- occasionally wrong
Llama 3 Alignment (Instruction-centric)
Alignment targets task success rather than conversational satisfaction.
task completion>style\text{task completion} > \text{style}task completion>style
Typical behavior:
- concise
- structured
- procedural reasoning
Synthetic Data Generation Implications
This is where the difference matters most for research.
Using Llama 2 for Synthetic Data
Good for:
- paraphrasing
- dialogue generation
- style transfer
Weak for:
- logical supervision
- reasoning trajectories
- training agents
Because errors are hard to detect — they look fluent.
Using Llama 3 for Synthetic Data
Good for:
- reasoning traces
- tool-use trajectories
- RL reward shaping
- verifier training
Because errors are structured — they expose incorrect steps.
Behavior Difference Example
Prompt: plan a multi-step solution
Llama 2:
produces a plausible explanation
Llama 3:
produces an executable plan
This is the difference between:
conversational intelligence vs operational intelligence
Practical Impact on Research
| Task | Llama 2 | Llama 3 |
|---|---|---|
| RAG | good | excellent |
| agent training | unstable | stable |
| reasoning RL | noisy reward | learnable reward |
| synthetic data | stylistic | procedural |
| tool use | brittle | robust |
For RL or agent pipelines, Llama 3 behaves more like a policy than a chatbot.
Conceptual Shift
We can interpret the evolution like this:
| Era | Model role |
|---|---|
| GPT-3 | language generator |
| Llama 2 | conversational assistant |
| Llama 3 | task executor |
Final Takeaway
The jump from Llama 2 → Llama 3 is not primarily scale.
It is a shift in objective:
Model text→Model behavior\text{Model text} \rightarrow \text{Model behavior}Model text→Model behavior
Llama 2 learned how humans talk.
Llama 3 learned how humans solve problems.
That makes it far more suitable for synthetic supervision and agent training.
References
Meta AI, Llama 2: Open Foundation and Fine-Tuned Chat Models, 2023
https://ai.meta.com/llama/
Meta AI, Introducing Llama 3, 2024
https://ai.meta.com/blog/meta-llama-3/
Touvron et al., Llama 2: Open Foundation and Fine-Tuned Chat Models
https://arxiv.org/abs/2307.09288
Meta AI, The Llama 3 Herd of Models
https://arxiv.org/abs/2407.21783