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:

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.