Alignment Training

Benched.ai Editorial Team

Alignment training is the set of procedures that steer a model toward behaviors consistent with human values, instructions, and safety constraints. It is typically applied after base pre-training.

Alignment Pipeline Phases

Phase	Goal	Typical Dataset	Common Methods
Supervised Fine-Tuning (SFT)	Teach following instructions	Human-written input/output pairs	Cross-entropy loss
Preference Collection	Gather comparisons between outputs	A/B annotator choices	Pairwise ranking
Reward Model Fit	Learn scalar reward from preferences	Preference pairs	Binary cross-entropy
Policy Optimization	Maximize reward while staying close to SFT policy	PPO, DPO, RLAIF	TRL-X, DeepSpeed RL
Safety Tuning	Reduce toxic or disallowed content	Adversarial prompts	Constitutional AI, supervised rejection

Comparison of Optimization Algorithms

Algorithm	Sample Efficiency	Training Stability	Implementation Complexity
PPO	Medium	Good with KL penalty	Moderate
DPO	High	Requires careful temperature	Low
Best-of-N sampling	Very high (no grad)	Stable	Very low

Design Trade-offs

Reinforcement methods boost helpfulness but risk reward hacking if reward model narrow.
Human annotation is costly; synthetic preference generation scales but may propagate bias.
KL-regularization towards the base model preserves knowledge at the expense of creativity.

Current Trends (2025)

Automated red-teaming datasets fed into alignment loops for continuous hardening.
Sparse finetuning (LoRA) cuts alignment compute 90 % relative to full-weight PPO.
Open-sourced reward models (Anthropic HH-RM v2) enable community audits.

Implementation Tips

Freeze embedding layers during alignment to prevent vocabulary drift.
Log KL divergence every minibatch; spikes often indicate reward-model overfitting.
Evaluate on safety benchmarks (HEL-Safety, Toxicity v2) each training epoch.