oddadmix/arabic-rag-chat-grpo-5K

Arabic multi-turn RAG conversations — GRPO pool (5,259 conversations)

The reinforcement-learning half of oddadmix/arabic-rag-chat-30K: same generator, same validator, same schema, disjoint companies. It exists so GRPO explores fresh knowledge bases instead of taking a second pass over material the SFT already memorised.

	conversations	turns	companies
this pool	5,259	14,018	309

Company-disjointness is exact and verified: this pool shares zero company_id values with either the train or test split of the 30K set, and those two share none with each other.

Composition

bucket	conversations
short	1,749
mid	1,749
long	1,761

55% of conversations carry a customer account record.

turn type	turns
single	3,937
followup	2,783
multipart	2,584
new-constraint	1,834
correction	1,163
unanswerable	1,059
partial	658

Fields

Identical to the 30K set — see that card for the full field list and the validation rules, which are the same here. In short: chunks are the numbered passages, account is the out-of-band customer record, and each turn carries gold_chunk_ids plus per-part key_facts that are validated to occur verbatim in the cited passage.

There is one split (data/all.jsonl) because this pool is never evaluated on — evaluation happens on the 30K set's test split, and the per-row artifacts are published separately as oddadmix/arabic-rag-chat-8k-eval.

How the reference recipe consumes it

One prompt per conversation, one sampled round — not every round. Rounds of the same conversation share a knowledge base and most of a prompt, so training on all of them would spend the rollout budget re-reading the same passages and make the effective batch far more correlated than its size suggests.

Prompts over 4,096 tokens are dropped before priority selection, not after: priority correlates hard with length, so filtering second would take the top n and then delete most of them. On the reference 50M run that left 3,046 usable prompts of 5,259 conversations — 2,213 dropped on length, and of what remained 1,270 multi-gold, 725 multi-part, 202 refusal.

Selection is failure-weighted toward multi-part and multi-gold turns, with numeric mixing hazard as the tie-break. Refusal rows are held at a fixed 15% share so the policy is not pushed toward declining more often, which is the cheapest way to game two of the seven rewards.

The rewards are programmatic — no judge in the training loop: number_grounding (1.0), refusal_reward (0.6), chrf_reward (0.8), sanity_reward (0.3), part_coverage (0.8), aggregation_recall (0.6), gold_coverage (1.0). part_coverage and aggregation_recall are what the per-part key_facts labels in this schema exist to make computable.

Caveats

All content is fictional and machine-generated; internally consistent per company, but not real. The teacher was instructed never to calculate — answers quote figures rather than combining them — so this set does not teach arithmetic, and number_grounding is correspondingly a strict verbatim string test rather than a numerical-reasoning check.

Measured GRPO gains from this pool were concentrated in per-passage coverage, not in aggregation: across three reward revisions part-coverage moved 50.1% → 58.5% while aggregation-recall stayed flat at 53.0% → 53.7%. If you are using this pool to attack multi-passage aggregation specifically, that is the honest prior — see the eval dataset for the full table.

© KAND CA 2026 — PROJECT NAWAH.