---
base_model: Navid-AI/Yehia-7B-preview
library_name: transformers
model_name: Shaer-adapters-grpo-friend-v1-easyfirst
license: other
tags:
- trl
- grpo
- arabic-poetry
- classical-arabic
- lora
datasets:
- Shaer-AI/ashaar-enhanced-desc-baseform-final-sft-lte20-min500-splits-grpo-meter-count-v1
- Shaer-AI/ashaar-with-enhanced-descriptions-baseform-final-sft-lte20-min500-splits
---

# Shaer-adapters-grpo-friend-v1-easyfirst

This repo is the easier judge-centered rerun that followed `Shaer-AI/Shaer-adapters-grpo-friend-v1`.

## Current Status As Of 2026-04-13

This repo is still the best result from the older multiplicative friend-style stage, but it has been surpassed as a training regime by later short-subset weighted-reward experiments.

The best completed GRPO stage is now `Shaer-AI/Shaer-adapters-grpo-short1k-no-trio-v2`, and the current unlaunched next-step candidate is the dual-judge `v5` setup prepared locally in `/root/workspace/Shaer/grpo`.

## Place In The Story

Project sequence:

1. `Shaer-AI/Shaer-adapters`
   clean SFT baseline
2. `Shaer-AI/Shaer-adapters-grpo`
   reward-hacked historical GRPO result
3. `Shaer-AI/Shaer-adapters-grpo-vnext`
   stricter anti-template rerun
4. `Shaer-AI/Shaer-adapters-grpo-friend-v1`
   first judge-centered rerun
5. `Shaer-AI/Shaer-adapters-grpo-friend-v1-easyfirst`
   easy-first judge-centered rerun
6. `Shaer-AI/Shaer-adapters-grpo-short1k-no-trio-v2`
   next weighted-reward rerun on the short `1-6` bayt subset

## What Data It Used

- base starting adapter:
  `Shaer-AI/Shaer-adapters`
- GRPO dataset artifact:
  `Shaer-AI/ashaar-enhanced-desc-baseform-final-sft-lte20-min500-splits-grpo-meter-count-v1`
- source poetry dataset:
  `Shaer-AI/ashaar-with-enhanced-descriptions-baseform-final-sft-lte20-min500-splits`
- train subset:
  easy-first dropped-trio subset, cap `2000` per surviving meter
- train size:
  `19001`
- eval bank:
  full `13`-meter eval bank, `104` rows total
- local run dir:
  `/root/workspace/Shaer/grpo/outputs/train/shaer_grpo_20260412_155553`

## Reward Used Here

This run kept the same friend-style multiplicative reward:

```text
hard_gate * meter * judge_quality * arabic_floor * count_floor * repeat_floor
```

The intent was:

- meter and judge as the core
- count kept inside the reward
- repeat and contamination softened into floors
- hard zero only for catastrophic junk

This stage kept semantics in the optimized reward through `judge_quality`, but the overall aggregation was still multiplicative, which later turned out to be too brittle and too permissive in some high-meter weak-meaning cases.

## Best Tracked Checkpoint

- step: `800`
- eval total: `0.2375`
- eval meter: `0.6896`
- eval judge quality: `0.3920`
- eval count adherence: `0.6201`
- eval arabic clean: `0.9423`
- eval repeat penalty: `0.6619`

Trained-meters-only snapshot at the best checkpoint:

- total: `0.2682`
- meter: `0.8028`
- judge quality: `0.3883`
- count adherence: `0.6018`
- joint good count: `8 / 80`

## What This Run Proved

This run was clearly healthier than `friend-v1`:

- meter recovered strongly on the trained meters
- the run found some genuinely promising samples
- the easy-first slice was much more learnable

But it still was not the final answer:

- judge quality stayed too low overall
- top-ranked samples could still look bad to humans
- high-meter / low-quality behavior was reduced, not solved

## Current Interpretation

For the paper story, this repo is the best result from the multiplicative judge-centered era. It proved that the judge-centered direction could become healthy on a friendlier train slice, but it also made the next problem obvious: semantic weakness could still survive behind strong structure.

## Why We Moved On

This repo directly motivated the next redesign:

- move from the multiplicative floor recipe to a smoother weighted reward
- keep meter strongest
- keep count inside the optimized reward but lighter
- keep judge focused on meaning, non-garbage, and relevance
- keep repeat as a soft guardrail
- train on the short easy-first `1k` per meter no-trio subset
- evaluate on a new short-only no-trio eval bank

That next stage was published as `Shaer-AI/Shaer-adapters-grpo-short1k-no-trio-v2`.

## Repo Contents

- root adapter files:
  the run-end adapter state
- `best-checkpoint/`:
  exported best checkpoint from this run

## Recommended Use

Use this repo as the best result from the friend-style multiplicative reward stage, but not as the final recommended Shaer GRPO model.