---
license: mit
---
# DeepScaleR-Medium-20k

RL training set of 20,000 DeepScaleR questions spanning difficulty (Gemma-3-4B-IT
pass rate at temp 1.0, 4 samples, LENIENT math_verify grading): the **same 10,000
4/4 questions as DeepScaleR-Easy-10k** + 3,000 from 3/4 + 3,000 from 2/4 + 4,000
from 1/4. 500 random questions (across all difficulties) held out as validation;
train 19,500.

Creation (seed 42 throughout): identical dedup-v2 pipeline as DeepScaleR-Easy-10k
(see that README): max-strength question dedup, conflicting-gold groups removed
via math_verify equivalence, one random copy per group -> 38,796 unique questions;
buckets sampled uniformly without replacement.

NOTE: because the 10k easy questions are shared, DeepScaleR-Easy-10k's val
questions may appear in this set's TRAIN split (and vice versa). Do not evaluate
a Medium-trained model on Easy-10k's val (or vice versa); each set is
self-consistent only with its own split.

Files: `*_train.parquet` and `*_val500.parquet` (500 unique held-out questions).
For mean@16 validation, replicate the val rows 16x locally. verl format:
data_source "math", prompt = single user message, reward_model.ground_truth,
extra_info carries lenient/strict pass counts.

Built by `rl-distill-scripts/data/build_deepscaler_easy_medium.py` in
JasonWei05/rl-distill.