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budget-internalization-iclr2027/gemma4-e2b-2k-grpo-superworm-s300

sourceHugging Faceapache-2.0updated 3d agoView on Hugging Face
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Gemma-4-E2B-it · 2k-token budget · grpo · superworm

RL-finetuned google/gemma-4-E2B-it trained for math reasoning under a 2,048-token generation budget. Released as part of an anonymous ICLR 2027 submission.

Run ID (petname): `superworm` · checkpoint step 300

Training

Base modelgoogle/gemma-4-E2B-it
AlgorithmGRPO (leave-one-out baseline, group reward normalization, token-level loss). Responses that hit the budget are truncated and scored as-is.
Generation budget (max_new_tokens)2,048
DataDeepScaleR (math), 3 epochs max
Batch32 prompts × 8 rollouts per step
OptimizerAdam, cosine LR schedule, peak LR 3e-6, 10 warmup steps
Steps300
Rewardbinary answer correctness (\boxed{} extraction)
Weights dtypeF32

Training prompt (user turn, rendered with the base model's chat template):

Think step-by-step to solve the following problem. Output your answer inside of \\boxed{} tags.:
{problem}

Let's think step-by-step

Usage

python
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "budget-internalization-iclr2027/gemma4-e2b-2k-grpo-superworm-s300"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="auto", device_map="auto")

With vLLM: vllm serve budget-internalization-iclr2027/gemma4-e2b-2k-grpo-superworm-s300

License

Inherits the license of the base model (google/gemma-4-E2B-it).