Implementation Roadmap to Frontier Lab Readiness
This note converts the learning sphere into a portfolio path. The goal is not to build a frontier model alone. The goal is to prove you can understand mechanisms, implement them, measure them, and communicate research-quality conclusions.
Phase 1: Tiny Transformer
Build:
- Tokenizer wrapper.
- Decoder-only transformer.
- RMSNorm.
- Causal attention.
- RoPE.
- SwiGLU.
- Sampling.
Measure:
- Training loss.
- Validation loss.
- Tokens/sec.
- Memory usage.
- Generation quality.
Notes:
Deliverable:
- Clean repo with a readable
model.py. - One note explaining tensor shapes and bugs you hit.
Phase 2: KV Cache And Inference
Build:
- Autoregressive generation without cache.
- Autoregressive generation with KV cache.
- MQA or GQA variant.
Measure:
- Time to first token.
- Tokens/sec during decode.
- KV-cache memory as context grows.
- Quality differences if using GQA/MQA.
Notes:
Deliverable:
- Benchmark plots for sequence length versus latency/memory.
Phase 3: Scaling Experiment
Build:
- Train several small models with different sizes and token budgets.
- Keep data and evaluation consistent.
Measure:
- Validation loss by compute.
- Loss by parameter count.
- Loss by token budget.
- Downstream toy-task performance.
Notes:
Deliverable:
- A short scaling-law memo with plots and limits.
Phase 4: MoE Toy Model
Build:
- Replace some MLP layers with routed expert MLPs.
- Implement top-k routing.
- Add load-balancing loss.
- Visualize expert usage.
Measure:
- Loss versus dense baseline.
- Active parameters.
- Expert load distribution.
- Routing entropy.
- Inference latency.
Notes:
Deliverable:
- Report showing when the toy MoE helps, fails, or merely adds complexity.
Phase 5: Long Context
Build:
- RoPE scaling or ALiBi variant.
- Sliding-window attention baseline.
- Long-context synthetic retrieval tasks.
Measure:
- Accuracy by answer position.
- Distractor sensitivity.
- Prefill latency.
- KV-cache growth.
Notes:
Deliverable:
- "Long context is not memory" memo with failure cases.
Phase 6: Post-Training
Build:
- SFT on a small instruction dataset.
- DPO or preference-tuning toy setup.
- Optional verifier-guided math task.
Measure:
- Base versus SFT versus preference-tuned behavior.
- Helpfulness.
- Refusal behavior.
- Format following.
- Overfitting.
Notes:
Deliverable:
- Before/after model behavior analysis with examples and evals.
Phase 7: Paper Reproduction
Pick one:
- FlashAttention-style memory benchmark.
- GQA versus MHA toy study.
- MoE routing/load-balancing experiment.
- DPO toy reproduction.
- Scaling-law reproduction.
Use:
Deliverable:
- Reproduction repo.
- Research memo.
- Clear statement of what did and did not reproduce.
Phase 8: Research Taste
Maintain:
- Open Research Questions Frontier LLM Architectures
- A paper log.
- A failed-experiment log.
- A "confusions" log.
Practice:
- For every paper, write one falsification experiment.
- For every experiment, write what you learned even if it failed.
- For every result, separate mechanism, data, scale, and systems effects.
What To Show A Frontier Lab
Strong signals:
- You can implement core mechanisms from scratch.
- You know what to measure.
- You understand systems costs.
- You can read papers critically.
- You can reproduce and falsify claims.
- You communicate clearly.
Weak signals:
- Only collecting papers.
- Only prompting models.
- Only repeating benchmark scores.
- Only knowing model names.