Can RL Teach Long-Horizon Reasoning to LLMs? Expressiveness Is Key
Source published ·Modelwire updated
Original coverage: arXiv cs.CL ↗·How Modelwire adds context
The development
Researchers have built ScaleLogic, a synthetic benchmark that isolates two independent variables in LLM reasoning: proof depth and logical expressiveness. By systematically varying task complexity across implication-only through first-order logic with quantifiers, the work reveals how RL training compute scales with reasoning difficulty. This addresses a long-standing gap in understanding whether current RL methods can push LLMs toward genuine long-horizon planning or merely memorize shallow patterns. The findings matter for anyone betting on RL as a path to more capable reasoning systems.
Modelwire’s AI-generated summary of coverage from arXiv cs.CL.
Modelwire analysis
ExplainerOur AI-generated reading of the wider context and the next developments to watch.
The core contribution is the decomposition itself: by treating proof depth and logical expressiveness as orthogonal axes, ScaleLogic can tell you whether a model is failing because the chain is too long or because the logic requires quantifiers, which prior benchmarks conflated into a single difficulty score.
This connects directly to two threads in recent coverage. The procedural execution study from May 1st showed accuracy collapsing from 61% to 20% as algorithm length grew, but couldn't isolate whether that collapse was a depth problem or a representational one. ScaleLogic is essentially the diagnostic tool that study was missing. Separately, the StraTA paper from May 7th proposes hierarchical RL as a fix for long-horizon reasoning failures. ScaleLogic could serve as a principled evaluation surface for claims like StraTA's, since it separates the variables those systems are implicitly trying to address.
Watch whether StraTA or similar hierarchical RL frameworks publish results on ScaleLogic or an equivalent decomposed benchmark within the next two quarters. If they do and gains appear on depth but not expressiveness, that would confirm hierarchical planning helps with sequence length but not with the harder logical structure problem.
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Coverage behind this analysis
These archive entries ground the connection in our analysis. They are ordered by source publication date, with links to our coverage and the original sources.
·arXiv cs.CL
When LLMs Stop Following Steps: A Diagnostic Study of Procedural Execution in Language Models
A new diagnostic benchmark reveals a critical gap in how LLMs execute multi-step procedures, with accuracy collapsing from 61% on short algorithms to 20% on 95-step tasks. The research isolates procedural faithfulness as distinct from reasoning ability, showing that models frequently skip steps, halt prematurely, or lose track of intermediate variables rather than making arithmetic…
MentionsScaleLogic · LLM · Reinforcement Learning
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