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budget-internalization-iclr2027
budget-internalization-iclr2027/gemma4-e2b-2k-grpo-superworm-s300
gemma4-e2b-2k-grpo-superworm-s300 is a any-to-any model from budget-internalization-iclr2027. Use it for the any-to-any task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
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.
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From the Hugging Face model README
superwormRL-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
| Base model | google/gemma-4-E2B-it |
| Algorithm | GRPO (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 |
| Data | DeepScaleR (math), 3 epochs max |
| Batch | 32 prompts × 8 rollouts per step |
| Optimizer | Adam, cosine LR schedule, peak LR 3e-6, 10 warmup steps |
| Steps | 300 |
| Reward | binary answer correctness (\boxed{} extraction) |
| Weights dtype | F32 |
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
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
Inherits the license of the base model (google/gemma-4-E2B-it).