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budget-internalization-iclr2027
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Model Details
Model Provider
budget-internalization-iclr2027
Model Tree
google/gemma-4-E2B-it
Input Modalities
Output Modalities
Supported Functionality
| Base model | google/gemma-4-E2B-it |
| Algorithm | GRPO (leave-one-out baseline, group reward normalization, token-level loss). Responses that hit the budget receive zero reward. |
Generation budget (max_new_tokens) | 2,048 |
| Data | Reasoning Gym tasks (procedurally generated reasoning problems, \boxed{} answers), 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 | Reasoning Gym task verifier on the \boxed{} answer |
| Weights dtype | F32 |
Prompts are the Reasoning Gym task prompts, rendered with the base model's chat template.
python
from transformers import AutoModelForCausalLM, AutoTokenizer repo = "budget-internalization-iclr2027/gemma4-e2b-2k-grpo-reasoninggym-smartcamel-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-reasoninggym-smartcamel-s300
Inherits the license of the base model (google/gemma-4-E2B-it).