Does reasoning efficiency transfer to tasks without ground truth dependency graphs?
This explores whether the techniques that make reasoning cheaper and shorter still hold up on open-ended tasks — the kind without a clean, verifiable chain of correct steps to lean on.
This reads the question as: does 'efficient reasoning' — trimming the chain of thought down to its load-bearing parts — survive when you leave behind tidy, checkable problems (arithmetic, puzzles) and enter tasks where there's no ground-truth scaffold telling you which step depends on which? The corpus suggests the efficiency travels surprisingly well, but for an unsettling reason: a lot of what looks like reasoning was never doing the dependency-tracking work in the first place.
Start with the optimistic evidence. Chain of Draft matches full chain-of-thought accuracy on arithmetic, symbolic, and commonsense tasks using just 7.6% of the tokens — the other 92% served style and documentation, not computation Can minimal reasoning chains match full explanations?. That implies the 'efficiency' isn't fragile decoration you'd lose on harder tasks; it was bloat all along. The unsettling version of the same finding: models trained on deliberately corrupted, irrelevant reasoning traces keep their accuracy and sometimes generalize *better* out of distribution Do reasoning traces need to be semantically correct?. If wrong traces teach as well as right ones, then the chain was functioning as computational scaffolding — a way to spend compute — not as a real dependency graph being faithfully traversed.
That reframes your question. The reason efficient reasoning transfers is that for many tasks there was no genuine dependency-tracking happening to begin with. Chain-of-thought is better understood as constrained imitation of reasoning *form* — reproducing familiar schemata from training — than as novel inference Does chain-of-thought reasoning reveal genuine inference or pattern matching?. And that imitation degrades predictably the moment you shift task, length, or format Does chain-of-thought reasoning actually generalize beyond training data?. So the honest answer to 'does it transfer to tasks without ground-truth structure?' is: it transfers as far as the training distribution does, and no further. When semantics are stripped from a task so only the logical skeleton remains, performance collapses even when the correct rules sit right there in context — these are semantic reasoners, not symbolic ones Do large language models reason symbolically or semantically?.
There's a sharper diagnostic worth knowing: failures cluster at instance-novelty boundaries, not complexity thresholds Do language models fail at reasoning due to complexity or novelty?. A model handles a long, intricate chain fine if it has seen similar instances, and stumbles on a short unfamiliar one. So 'tasks without ground-truth dependency graphs' is really code for 'tasks far from anything the model fit patterns on' — and that, not raw difficulty, is where efficient and verbose reasoning alike break down. Some apparent collapses are even execution bottlenecks rather than reasoning ones: models that know an algorithm still can't run it for many steps in pure text, and giving them tools restores performance past the supposed cliff Are reasoning model collapses really failures of reasoning?.
The constructive thread for tasks lacking a built-in dependency graph is to *supply* one externally. Externalizing reasoning into knowledge-graph triples lets a GPT-4o-mini-class model jump 29% on hard GAIA tasks, because the structure becomes explicit, inspectable, and quality-controllable instead of implicit in the token stream Can structuring reasoning as knowledge graphs help smaller models solve complex tasks?. Symbolic rules derived from graph topology give the model a navigational plan it otherwise lacks Can symbolic rules from knowledge graphs guide complex reasoning?. The takeaway you didn't know you wanted: efficient reasoning transfers cheaply precisely on tasks where the chain was never doing real work — and on the tasks where you actually need a dependency graph, the move isn't a longer chain, it's building the graph outside the model.
Sources 9 notes
Chain of Draft achieves equivalent accuracy to standard chain-of-thought on arithmetic, symbolic, and commonsense tasks while using only 7.6% of tokens. The 92.4% of removed tokens served style and documentation, not computation.
Models trained on systematically irrelevant traces maintain solution accuracy and sometimes improve out-of-distribution generalization, suggesting traces function as computational scaffolding rather than meaningful reasoning steps.
CoT works by constraining models to reproduce familiar reasoning patterns from training, not by enabling novel symbolic reasoning. Performance degrades predictably under distribution shifts—the signature of imitation rather than capability emergence.
DataAlchemy experiments show CoT fails systematically under distributional shifts in task, length, and format. Models produce fluent but logically inconsistent reasoning — imitating reasoning form without valid underlying logic.
When semantic content is decoupled from reasoning tasks, LLM performance collapses even with correct rules in context. Models rely on parametric commonsense and token associations rather than formal logical manipulation, constraining reasoning to training distribution semantics.
LRMs don't break at complexity thresholds but at instance-novelty boundaries. Models fit instance-based patterns rather than generalizable algorithms, so any reasoning chain succeeds if trained on similar instances, regardless of length.
Models confined to text-only generation cannot execute multi-step procedures at scale, even when they know the underlying algorithm. Tool-enabled models solve problems beyond the supposed reasoning cliff, suggesting the bottleneck is procedural execution bandwidth.
Knowledge Graph of Thoughts (KGoT) achieves 29% improvement on GAIA Level 3 tasks using GPT-4o mini by externalizing reasoning into iteratively constructed KG triples. The approach improves transparency, reduces bias, and enables quality control over reasoning steps.
SymAgent derives symbolic rules from KG structure using LLM reasoning to create navigational plans that align natural language with graph topology. This approach captures structural reasoning patterns explicitly, outperforming retrieval methods that rely on semantic similarity alone.