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Model: narinzar/grpo-finetune-walkthrough
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+pipeline_tag: text-generation
+tags:
+  - grpo
+  - reinforcement-learning
+  - rlvr
+  - text-generation
+---
+
+# grpo-finetune-walkthrough
+
+A `Qwen/Qwen2.5-0.5B-Instruct` policy fine-tuned with GRPO (Group Relative
+Policy Optimization) on a verifiable arithmetic task, using a from-scratch
+implementation of the group-relative advantage and policy-gradient loss (no TRL
+for the core math). Code: https://github.com/narinzar/grpo-finetune-walkthrough
+
+## What this is
+
+This is a minimal 20-step demonstration run on a single RTX 5090, not a
+converged model. Its purpose is to show that the from-scratch GRPO training loop
+works end to end and produces a rising reward signal. Run configuration: 20
+steps, group size 4, 2 prompts per step, `max_new_tokens` 64, evaluated on 80
+held-out arithmetic items.
+
+## Results
+
+| Metric                           | Value        |
+| -------------------------------- | ------------ |
+| Mean reward, step 0              | 0.125        |
+| Mean reward, step 19             | 0.250        |
+| Eval exact-match accuracy before | 0.000 (0/80) |
+| Eval exact-match accuracy after  | 0.013 (1/80) |
+
+The mean reward roughly doubles across the run (0.125 to 0.25), confirming the
+GRPO update is optimizing the policy in the right direction. At this tiny scale
+the eval accuracy barely moves (0/80 to 1/80) and format compliance has not
+emerged yet. Real accuracy and format gains need far more steps, many more
+prompts per step, and longer generations. Treat these numbers as a correctness
+check on the training loop, not as a measure of how well GRPO can do on this
+task.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("narinzar/grpo-finetune-walkthrough")
+tokenizer = AutoTokenizer.from_pretrained("narinzar/grpo-finetune-walkthrough")
+```
+
+- Base model: `Qwen/Qwen2.5-0.5B-Instruct`
+- Task: text generation / GRPO RL fine-tuning on verifiable arithmetic
+- License: apache-2.0