---
name: trace-to-training-data
description: Convert evaluation traces and production logs into SFT examples and preference pairs. Use when graded traces or failure examples exist and need to become training data, when applying rejection sampling to model outputs, or when building DPO pairs from passing and failing runs.
---
# Trace To Training Data
This skill assumes `eval-harness-first`
already graded the traces being
converted here — goldens, graders,
and `runs/<run-id>/results.json`
all exist before conversion
starts. This is the flywheel edge
that skill names in its own flow:
"the same labeled traces become
the training set." Conversion
happens here; grading already
happened upstream.
**Input:** graded traces —
`eval/goldens.jsonl` plus
`runs/<run-id>/results.json`, each
row carrying a `task_id`, a
`verdict` from the grader, and a
`reward` when the task supports a
scalar score (judge score,
execution partial-credit, or an
RLVR verifier):
```json
{"task_id": "t-042", "trace_id": "t-042-a3",
"messages": [{"role": "user", "content": "..."}],
"verdict": "pass", "reward": 0.91,
"grader": "exact_match"}
```
**Output format:** rows shaped
exactly like `dataset-curation`'s
Format Selection table — SFT
`messages` rows or DPO
`prompt`/`chosen`/`rejected`
pairs — so this skill's output is
that skill's input with no
reshaping step in between.
## The Principle
The eval harness already did the
labeling work: every trace in
`results.json` carries a verdict,
and often a reward, before this
skill ever touches it. Converting
a graded trace into a training
row is mechanical — pick a shape
from `dataset-curation`'s table,
map fields, write JSONL.
**Curation is the work that
remains** — which traces clear a
quality bar, which pairs are
informative, and which rows must
never enter the training set at
all.
Treat any conversion step that
requires re-judging a trace as a
sign the harness is missing a
grader, not a gap this skill
should paper over. A trace with
no verdict or reward isn't
convertible yet — route it back
to `eval-harness-first` first,
don't hand-label it here to
unblock conversion.
## SFT From Traces
- **Keep the top-reward fraction
of successful trajectories**,
not every passing one. Rank
passing traces by reward and
take a fraction (the
Agent-lightning pattern) rather
than every trace that merely
cleared the pass bar — a trace
that barely passed is a weaker
SFT signal than one that scored
well above threshold.
- **Expert-corrected failures
become gold SFT examples
directly** (the Langfuse
pattern) — when a human edits a
failing trace's output into a
correct one, that correction
needs no reward threshold; a
human already validated it.
Route corrections straight into
the SFT set.
- **Step-level masking beats
whole-trajectory discard for
multi-step traces.** When only
some steps in a multi-step
trajectory are bad, mask the
loss on the bad steps and keep
the good ones, rather than
discarding the whole trajectory.
SRFT reports 32.2% vs. 30.9% on
SWE-bench for step-level critic
masking over trajectory discard
— a real, if modest, gap from
the finer-grained cut.
## Preference Pairs From Traces
- **Build pairs from
passing-vs-failing trajectories
on the SAME task**, never from
unrelated best- and
worst-scoring traces pulled
across different tasks —
cross-task pairs teach the
model to prefer one task over
another, not one response over
another.
- **Select the rejected member at
μ−2σ of the reward distribution
for that task, never the
absolute minimum.**
`preference-optimization`'s
Pair Construction section owns
the full selection formula;
this skill supplies the graded
trajectories it consumes.
- **Judge-scored delta selection
cuts pair volume without
cutting signal.** Score each
candidate pair by
chosen-minus-rejected judge
delta and keep only the
highest-delta subset — the top
5k of a 16.5k candidate pool
matched the full pool's
downstream result. Build the
full candidate set first, then
filter by delta; don't cap
generation at 5k up front.
## Hygiene
- **Scan for secrets and PII before any row ships,
and redact what's found.** Traces sourced from
production logs can carry credentials, API keys,
tokens, or customer data — run a secret/PII scan
over every SFT and DPO row and redact matches;
conversion fails closed (the row is dropped, not
shipped with the raw content) if sensitive fields
remain after redaction. Never commit secrets.
- **Eval goldens must never leak
into training data.** Hold
every `eval/goldens.jsonl` ID
out of every converted SFT and
DPO set — a trace that also
appears as a golden trains on
the exact item the checkpoint
gets graded against later,
silently inflating every
subsequent eval run.
- **Dedup against the training
set**, not just within the
newly converted rows —
exact-match or
embedding-similarity, matching
`dataset-curation`'s dedup
method field, run against
whatever training data already
exists before this batch merges
in.
- **Provenance goes into the
dataset card.** Every converted
row must trace back to its
source `run_id` and `trace_id`
— `dataset-curation`'s
Provenance field checks for
exactly this link back to
`trace-to-training-data`
output; a row with no traceable
source isn't ready to merge.
## Related Skills
- `eval-harness-first` — produces
the graded traces this skill
converts; a trace with no
verdict or reward isn't
convertible yet, route it back
there before conversion.
- `dataset-curation` — owns the
target formats and the dataset
card this skill's provenance
data feeds; converted rows must
match its Format Selection
table field names exactly, not
an approximation of them.
- `preference-optimization` —
consumes the DPO pairs this
skill builds and owns the full
μ−2σ rejection-selection
formula referenced above.
Worked JSONL-to-JSONL conversions
— graded trace to SFT row, trace
pair to DPO pair, correction to
SFT row, the rejection-sampling
loop, and the goldens-holdout
check — live in
`references/conversion-recipes.md`.