Gemma-4-E2B-it · 4k-token budget · grpo · merrydingo
RL-finetuned google/gemma-4-E2B-it trained for math reasoning under a 4,096-token generation budget. Released as part of an anonymous ICLR 2027 submission.
Run ID (petname): merrydingo · checkpoint step 300
Training
| 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) | 4,096 |
| 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
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "budget-internalization-iclr2027/gemma4-e2b-4k-grpo-merrydingo-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-4k-grpo-merrydingo-s300
License
Inherits the license of the base model (google/gemma-4-E2B-it).