---
base_model: Navid-AI/Yehia-7B-preview
library_name: transformers
model_name: Shaer-adapters-grpo-short1k-no-trio-v2
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-short1k-no-trio-v2

This repo is the current best completed GRPO stage in the Shaer project.

## Current Status As Of 2026-04-13

This is still the best completed GRPO run in the project.

Later runs `v3` and `v4` were useful diagnostically, but neither surpassed `v2` as a finished model. 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`
   trusted clean SFT baseline
2. `Shaer-AI/Shaer-adapters-grpo`
   historically important but reward-hacked GRPO stage
3. `Shaer-AI/Shaer-adapters-grpo-vnext`
   first strict anti-hack rerun
4. `Shaer-AI/Shaer-adapters-grpo-friend-v1`
   first judge-centered GRPO run
5. `Shaer-AI/Shaer-adapters-grpo-friend-v1-easyfirst`
   healthier multiplicative judge-centered rerun
6. `Shaer-AI/Shaer-adapters-grpo-short1k-no-trio-v2`
   smoother weighted-reward rerun on a short easy-first subset
7. `Shaer-AI/Shaer-adapters-grpo-short1k-no-trio-v3`
   meter-times-judge-core rerun on the same short subset

This repo is the first stage where the project adopted both:

- the short no-trio train and eval regime
- a smoother weighted reward instead of the old multiplicative floor recipe

## What Data This Run 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:
  short only `1-6` bayts
- meter coverage:
  dropped `المديد`, `المنسرح`, `الهزج`
- sampling policy:
  easy-first with medium fill
- target cap:
  `1000` rows per surviving meter
- realized train size:
  `9955`
- eval bank:
  short-only no-trio, `80` rows total
- local run dir:
  `/root/workspace/Shaer/grpo/outputs/train/shaer_grpo_20260412_221146`

## Reward Used Here

This run introduced the weighted reward:

```text
reward_train = hard_gate * (
    0.45 * meter
  + 0.15 * count_adherence
  + 0.30 * judge_quality
  + 0.10 * repeat_soft
)
```

Key ideas:

- keep meter as the strongest signal
- keep count inside the optimized reward, but lighter
- use a focused Arabic judge for meaning, non-garbage, and relevance
- keep repeat as a soft guardrail
- use only a minimal hard gate for catastrophic junk

Judge setup in this stage:

- focused Arabic judge
- scores meaning
- scores whether the poem is not garbage
- scores relevance to the description

## Best Checkpoint

- best checkpoint:
  `checkpoint-3200`
- best eval total:
  `0.7862`
- best eval meter:
  `0.9395`
- best eval judge quality:
  `0.4445`
- best eval count adherence:
  `0.9919`
- best eval repeat soft:
  `0.9004`
- best eval hard gate:
  `0.9875`

Final eval at step `3300`:

- total: `0.7728`
- meter: `0.9251`
- judge quality: `0.4210`
- count adherence: `0.9975`
- repeat soft: `0.9031`
- hard gate: `0.9750`

## What This Run Proved

This is the healthiest completed GRPO regime so far in the project:

- meter became very strong
- count adherence became excellent
- contamination was much better controlled
- repeat-soft visibly exposed template collapse

But this is still not the final flagship paper model:

- strong semantic samples are still too sparse
- high-meter weak poems still survive
- semantic quality remains the main bottleneck

## Why We Moved On

Manual inspection of this run made the next issue unusually clear:

- the additive reward was healthier than the multiplicative one
- but a poem could still get a fairly good total score from:
  - excellent meter
  - strong count adherence
  - strong repeat-soft
  - weak semantic judge

That led directly to the next stage:

- `Shaer-AI/Shaer-adapters-grpo-short1k-no-trio-v3`

whose main change is:

```text
reward_train = hard_gate * (
    0.65 * (meter * judge_quality)
  + 0.20 * count_adherence
  + 0.15 * repeat_soft
)
```

The whole point of that next run is to make semantic weakness unable to hide behind great meter.

## Useful Local Artifacts

- full run dir:
  `/root/workspace/Shaer/grpo/outputs/train/shaer_grpo_20260412_221146`
- manual eval inspection:
  `eval_manual_inspection.md`
- run summary:
  `run_summary.json`

## Recommended Use

Use this repo as the current best completed GRPO stage in the Shaer project.

For paper writing, present it as:

- the strongest completed GRPO regime so far
- clearly better than the older reward-hacked and multiplicative stages
- still short of the final desired semantic quality bar