timeseries-encoder-fused-8b-llms-answering-questions-require-chaining-several-temporal CLM-0224For timeseries-encoder-fused 8B LLMs answering questions that require chaining several temporal primitives, supervised fine-tuning on worked decompositions only helps on composition families whose decompositions were demonstrated, while RLVR with outcome-verifiable rewards on top of that checkpoint also lifts composition families that received no demonstration (+20.1 pp) and held-out compositions (+9.6 pp).
Capability: Following an unfamiliar procedure
Observed on
Qwen3-8B with a timeseries encoder, trained entirely on synthetic atomic and composite QA with programmatic ground truth; evaluated on synthetic compositional splits plus TSEvol, S.
Sources
- Measured on ~5k synthetic composite and ~8k OOD test samples against text LLM, temporal-reasoning and TS-native baselines; includes a rejection-fine-tuning control on the same 10K prompts (0.567 vs 0.597 RL) and a data-scaling comparison (110K extra SFT vs 10K RL). Controls are partial: the RL arm builds on the SFT arm, and reported budgets differ across comparisons.
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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: medium. Falsifier as drafted: A matched-compute, matched-data continued-SFT or rejection-sampling arm from the same checkpoint matching RLVR's gain on reward-only and held-out composition splits. Proposed technique, not catalogued: RLVR on synthetic compositional timeseries QA with answer-type-specific rewards. Automatic check flagged: figures not in the source: 9.6.