LLM Training Knowledge Base

The source notes point toward a practical view of LLM development: strong product behavior is often shaped more by post-training and evaluation discipline than by raw pretraining scale alone.

The important question is not just whether the model knows more, but whether it is consistently useful, controllable, benchmark-honest, tool-capable, and robust under real inference conditions.

Post-training should be treated as a multi-objective optimization problem. A useful model needs reasoning quality, instruction following, response quality, tool correctness, and broad capability retention at the same time.

No single eval should dominate the training story. Different benchmarks test different slices of model behavior.

• Arena-style evaluations for broad preference-based quality comparison
• Hard reasoning or knowledge evaluations such as GPQA-like tasks
• Math-oriented evaluations for structured reasoning behavior
• Pass@k framing when stochastic sampling matters

Reasoning gains are often connected to reinforcement-style post-training, verifier or reward-guided optimization, and the distinction between a pure RL route and a supervised cold start.

• Cold-start supervised data can stabilize later RL.
• Pure RL gains can produce unstable or awkward behavior if left unchecked.
• Reward design matters as much as the optimizer.
• Reasoning should be measured alongside readability and utility.

A strong model should solve hard tasks while still obeying constraints, preserving structure, and communicating clearly.

• Follow explicit constraints without drifting into over-refusal.
• Preserve formatting, schema, and style requirements.
• Improve naturalness without sacrificing precision.
• Track response quality separately from benchmark accuracy.

Tool use should be evaluated explicitly, because free-form reasoning improvements do not automatically preserve structured calling behavior.

• Train tool selection and argument formatting directly.
• Evaluate exact schema adherence, not just intent.
• Test multi-turn tool use under ambiguity.
• Protect tool reliability through later post-training stages.

Multilingual performance often degrades if post-training becomes too English-centric. If multilingual quality matters, it needs explicit representation in both the training mixture and the eval suite.

Long-context quality should be measured through retrieval fidelity, instruction persistence, and degradation under distractors rather than through context length claims alone.

• Decontaminate aggressively for benchmark integrity.
• Track mixture weights across reasoning, tools, multilingual, and chat data.
• Use smaller curated datasets carefully and evaluate them with the right sampling logic.
• Prefer explicit dataset taxonomies over a single undifferentiated blob.

Chat templates and runtime compatibility matter more than teams often admit. Training-time and inference-time assumptions need to align.

• Align training and serving chat templates.
• Validate tool formatting in the actual runtime stack.
• Document BOS/EOS and special-token assumptions.
• Test in the target inference engine before calling a run successful.

1. Define capability buckets.
2. Build the evaluation suite first.
3. Create a supervised post-training seed.
4. Add reasoning optimization while watching regressions elsewhere.
5. Rebalance the mixture whenever gains look narrow or suspicious.
6. Validate the exact chat template and runtime intended for deployment.

In short: do not optimize for one impressive score. Optimize for a reliable system.