Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining
Paper • 2504.07912 • Published • 1
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Echo Chamber math-only pretraining data, tokenized
The pretraining sources of Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining
(Zhao, Meterez et al., COLM 2025, arXiv:2504.07912), tokenized exactly
as in the authors' code (rosieyzh/openrlhf-pretrain,
pretraining/data/*_to_tokens.py): datatrove 0.4.0 DocumentTokenizer with the Llama-2 tokenizer
(vocab 32000; the copy shipped with the authors' checkpoints, identical to meta-llama/Llama-2-7b-hf),
one </s> (id 2) after every document, no BOS, documents shuffled with seed 0. Question-answer sources are
question + "\n\n" + solution with no chat template. Produced by
PRETRAINING-PASSK/OLMo-mathonly/pretraining/passk/flame/data/tokenize_all.sh.
Layout
Each piece is one directory of datatrove shards: <rank>_<sub>_shuffled.ds (raw little-endian uint16
token ids, 2 bytes per token), .ds.index (document end offsets) and .ds.metadata. The OLMo fork reads
the .ds files as memmaps (data.memmap_dtype: uint16); MANIFEST.tsv lists every file with its size and
token count.
| piece | source and text construction | shards | tokens |
|---|---|---|---|
MetaMathQA-tokenized/ |
meta-math/MetaMathQA-395K: query + \n\n + response | 1 | 0.101B |
algebraic-stack-tokenized/ |
EleutherAI/proof-pile-2, algebraic-stack/train (documents) | 61 | 10.900B |
eval/gsm8k-test-tokenized/ |
openai/gsm8k main test: question + \n\n + answer (evaluation only) | 1 | 0.000B |
eval/math500-tokenized/ |
HuggingFaceH4/MATH-500 test: problem + \n\n + solution (evaluation only) | 1 | 0.000B |
eval/openmathinstruct1-validation-tokenized/ |
nvidia/OpenMathInstruct-1, correct_solutions/validation (evaluation only) | 1 | 0.056B |
finemath3-tokenized/ |
HuggingFaceTB/finemath, finemath-3plus (documents) | 64 | 41.760B |
openmathinstruct1-tokenized/ |
nvidia/OpenMathInstruct-1, correct_solutions/train: question + \n\n + generated_solution | 1 | 0.369B |
openmathinstruct2-tokenized/ |
nvidia/OpenMathInstruct-2, train (14M rows): problem + \n\n + generated_solution | 32 | 5.672B |
tinygsm-tokenized/ |
TinyGSM/TinyGSM: question + \n\n + code | 17 | 2.669B |
| training sources total | 61.471B |
The paper's mixtures (model-card naming: as Algebraic-Stack, fm3 FineMath-3+, tg TinyGSM, omi1/omi2
OpenMathInstruct-1/2, mmqa MetaMathQA, <n>x = n passes) are built from these pieces as directories of
symlinks, n copies per pass (pretraining/passk/data/make_mix.py, the authors' mix_datasets.py), and trained for
one epoch at 512 x 2048 tokens per step.
Note: the authors' OpenMathInstruct-2 script globs *.parquet, which on the hub also matches the
train_1M/2M/5M subset files (duplicates of rows in train); this copy holds the 14M-row train split
only, as the paper describes.
Fetching
bash PRETRAINING-PASSK/OLMo-mathonly/pretraining/passk/flame/data/download_from_hf.sh /tmp/$USER-mathonly-data # parallel resumable curl
Licenses follow the upstream datasets (ODC-By for FineMath, the Proof-Pile-2 subset licenses for Algebraic-Stack, CC-BY-NC/CC-BY for the NVIDIA OpenMathInstruct sets, MIT for TinyGSM/MetaMathQA, MIT for GSM8K and MATH).
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