fine-tuning-14b-model-knowledge-graph-derived-multiple-choice-qa-single-medical-domain CLM-0390When fine-tuning a 14B model on knowledge-graph-derived multiple-choice QA for a single medical domain, attaching supporting triples from the same source text chunk to each target triple during supervision, and repairing residual one-hop failures before reinforcement learning, gives higher accuracy on held-out 3-, 4- and 5-hop questions than training on isolated triples or running RL from an unrepaired checkpoint.
Capability: Carrying a chain of inference through to its conclusion
Observed on
Qwen3-14B with LoRA, synthetic multiple-choice QA generated from GraphMERT-extracted disease KGs (Gastroparesis, Diabetes) of roughly 6,000 triples each; RL on 1-hop and 2-hop item.
Sources
- Validated by: Accuracy on 8,000 generated 3-hop, 4-hop and 5-hop QA items per setting, across two disease KGs, comparing KG-grounded vs context-grounded supervision and RL from repaired vs unrepaired SFT checkpoints, plus an answer-option shuffling robustness check.. Measured on two disease KGs with matched training configuration, so the supervision-type comparison is isolated. [truncated]
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Related claims
- In a hierarchical multi-agent system where a controller gates acceptance of worker outputs on semantic entropy and semantic density computed over resampled completions, the intervention helps only where failures come from semantic instability (vague or contradictory outputs) and hurts where they come from evidence selection or reasoning-chain errors \u2014 gaining on ambiguity/hallucination-prone benchmarks while losing 10-14 points on multi-hop QA.Getting more from several models than from one · reviewed by AI
- When RL-training a central-agent multi-agent harness with 1.5B-3B Qwen2.5 backbones on multi-hop QA, optimizing only the coordinating central policy and leaving sub-agents frozen scores higher than jointly training central and sub-agent policies (average F1 47.79 vs 45.36, accuracy 42.88 vs 40.49 at 3B).Getting more from several models than from one · reviewed by AI
- When training 4B-8B Qwen3 agents with group-based RL (GRPO, DAPO, GSPO) on sparse-reward long-horizon tasks, injecting hints into the environment's feedback — procedural action guidance in early episode steps and early training, switching to enriched state observations in later steps and later training — raises success on the unmodified environments by a few points on average, while lowering performance on tasks that require questioning whether the request is answerable at all.Completing tasks that take many steps or many hours · reviewed by AI
- For agentic retrieval-augmented multi-hop QA with 7B-class policy models, scoring each intermediate step for both logical validity and grounding in the retrieved passages — rather than scoring steps by the final answer — yields higher EM/F1 than outcome-reward and final-answer-based process-reward training, because it removes preference pairs where flawed retrieval happened to reach the right answer.Carrying a chain of inference through to its conclusion · reviewed by AI
- For multi-hop factual QA with Qwen2.5-7B/Qwen3-8B given a deliberately insufficient retrieved triple subgraph, training a policy with RL to insert bridging triples that let a separate frozen answerer reach the gold answer raises answer accuracy over standard GRPO and over prompting methods like Self-Ask and RECITE, and the improvement carries over to direct chain-of-thought answering with no triples inserted at test time.Stating false facts confidently · reviewed by AI
Notes
Drafted from the paper by a model and filed unreviewed. Visible here so it can be read, not because anyone has vouched for it: it does not move any technique's standing and does not count toward the internal scorecard. Drafted confidence: low. Falsifier as drafted: On the same KGs and base model, context-augmented supervision and pre-RL repair fail to beat KG-only supervision and RL from unrepaired checkpoints on higher-hop accuracy, or the gain disappears under option shuffling or on human-written multi-hop questions.