---
base_model: Navid-AI/Yehia-7B-preview
library_name: transformers
model_name: Shaer-adapters-grpo-short1k-no-trio-v4
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-v4

This repo is a stopped post-`v3` experiment in the Shaer project.

It exists because `v3` made the next problem very clear: the judge still over-credited awkward poems. `v4` tried to solve that by making low-judge poems much harder to rescue with count and repeat. That logic looked good offline, but the online run turned out too harsh and did not learn well enough.

## Current Status As Of 2026-04-13

This model is not the recommended next direction to continue training from.

The best completed GRPO stage is still `Shaer-AI/Shaer-adapters-grpo-short1k-no-trio-v2`. 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 base
2. `Shaer-AI/Shaer-adapters-grpo`
   historically hacked early GRPO stage
3. `Shaer-AI/Shaer-adapters-grpo-vnext`
   structure-side anti-hack repair stage
4. `Shaer-AI/Shaer-adapters-grpo-friend-v1`
   first judge-centered 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`
   best completed weighted short-subset run
7. `Shaer-AI/Shaer-adapters-grpo-short1k-no-trio-v3`
   stopped meter-times-judge-core stage
8. `Shaer-AI/Shaer-adapters-grpo-short1k-no-trio-v4`
   stopped judge-gated-helper stage

## 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_20260413_124347`

## Reward Used In This Run

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

Why this was tried:

- keep `meter * judge` as the core from `v3`
- prevent low-judge poems from coasting on count and repeat

## Best And Final Tracked Metrics

Best eval:

- step: `50`
- total: `0.2064`
- meter: `0.5631`
- judge: `0.2937`
- count adherence: `0.9606`
- repeat soft: `0.9909`
- hard gate: `0.9125`

Latest eval before stop:

- step: `250`
- total: `0.1951`
- meter: `0.4753`
- judge: `0.3066`
- count adherence: `0.9763`
- repeat soft: `0.9909`
- hard gate: `0.9250`

Train stopped around:

- train step: `250`

## Why We Stopped

- the run did not look blatantly hacked
- but the online learning trend was poor
- meter stayed too low
- strong-sample rate was weak
- offline reward validation was healthier than the actual online training behavior

So this run is best interpreted as:

- a useful reward-shape experiment
- evidence that the `v4` judge-gated helper design was too harsh in practice

## What Came Next

The repo was then prepared for a dual-judge `v5` candidate:

- one judge for meaning/description fit
- one judge for naturalness / anti-template quality

with the new proposed reward:

```text
hard_gate * (
    0.45 * (meter * judge_meaning_fit)
  + 0.20 * judge_naturalness
  + 0.20 * (count_adherence * judge_meaning_fit)
  + 0.15 * (repeat_soft * judge_naturalness)
)
```

Validation artifacts for that next candidate live under:

- `dual_judge_reward_validation/`