HuggingEnvs
/

geoguesser-qwen3.5-4b-grpo-v3

@@ -1,209 +1,83 @@
 ---
 base_model: Qwen/Qwen3.5-4B
 library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:Qwen/Qwen3.5-4B
-- grpo
-- lora
-- transformers
-- trl
 ---
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-### Framework versions
-- PEFT 0.20.0

 ---
 base_model: Qwen/Qwen3.5-4B
 library_name: peft
+license: apache-2.0
+pipeline_tag: image-text-to-text
 tags:
+  - openenv
+  - trl
+  - grpo
+  - reinforcement-learning
+  - lora
+  - visual-geolocation
+  - geoguessr
+  - ablation
+datasets:
+  - HuggingEnvs/geoguesser-tasks
+model-index:
+  - name: geoguesser-qwen3.5-4b-grpo-v3
+    results:
+      - task:
+          type: image-text-to-text
+          name: Visual geolocation
+        dataset:
+          name: GeoGuesser eval split (200 held-out tasks)
+          type: HuggingEnvs/geoguesser-tasks
+        metrics:
+          - type: reward
+            value: 0.5526
+            name: mean-of-4 reward (best checkpoint, step 175)
 ---
+# GeoGuesser · Qwen3.5-4B · GRPO run 3 (the ablation)
+A LoRA adapter for [GeoGuessr](https://huggingface.co/spaces/HuggingEnvs/geoguesser-env), trained to
+answer a question rather than to be the best model: **why did run 1 work?**
+If you want the model that scores, use
+**[`geoguesser-qwen3.5-4b-grpo`](https://huggingface.co/HuggingEnvs/geoguesser-qwen3.5-4b-grpo)**
+(run 1, 0.6445). This one is here so the ablation is reproducible.
+## What it answers
+Run 1's training dynamics looked alarming: entropy collapsed, the reward spread inside each group
+went to almost nothing, and grad norm peaked above 11. Run 2 was designed to suppress exactly that
+(`scale_rewards="none"`, `beta=0.02`, two tasks per optimizer step). It trained cleanly and gained a
+fifth as much. Run 3 reverted only the first two of those settings.
+| | run 1 | run 2 | run 3 (this) |
+|---|---:|---:|---:|
+| `scale_rewards` | `group` | `none` | `group` |
+| `beta` | 0 | 0.02 | 0 |
+| tasks per step | 1 | 2 | 2 |
+| action cost scale | 1.0 | 0.2 | 0.2 |
+| median within-group spread | 0.016 | 0.193 | 0.078 |
+| peak grad norm | 11.25 | 0.16 | 6.77 |
+| **paired gain over its own base** | **+0.1620** | +0.0326 | **+0.0717** |
+So the instability was not a bug to suppress: it was where most of the learning came from. Run 3
+recovers about 44% of run 1's gain by putting it back, which narrows the cause to those two settings
+plus the action cost, and is the reason the next experiment is a cost sweep.
+Best checkpoint here is step 175 at **0.5526** mean-of-4 (its own base arm scored 0.4809). Scores
+are on the training reward curve, recomputed from raw distance; see the project README.
+## Training
+| | |
+|---|---|
+| base | `Qwen/Qwen3.5-4B` |
+| method | GRPO (TRL), `environment_factory` multi-turn tool calling |
+| LoRA | r=16, α=32, dropout 0.05, on `q/k/v/o_proj` |
+| steps | 300, two tasks per optimizer step (`ACCUM=4`), `NUM_GENERATIONS=8` |
+| turns | 12 max · image 448 px |
+| optimiser | LR 3e-5, temperature 1.0, `beta=0`, `scale_rewards="group"`, `COST_SCALE=0.2` |
+| environment | [`HuggingEnvs/geoguesser-env`](https://huggingface.co/spaces/HuggingEnvs/geoguesser-env), over HTTP |
+## Everything else
+- **[The write-up](https://huggingface.co/spaces/HuggingEnvs/geoguesser-article)**, including why these two settings mattered so much
+- **[All four runs](https://huggingface.co/spaces/HuggingEnvs/geoguesser-trackio)** on one axis
+- **[Code and exact commands](https://github.com/adithya-s-k/HuggingEnvs/tree/main/03-geoguesser)**
+Imagery is Mapillary, CC BY-SA 4.0.