Does the reversal curse stem from the same one-way commitment architecture?
This reads the question as asking whether the reversal curse — a model trained on 'A is B' failing to infer 'B is A' — shares a root cause with the broader autoregressive, forward-only way LLMs commit to a sequence left-to-right.
This explores whether the reversal curse and a model's general inability to back out of a forward commitment are the same problem wearing two faces. Up front, a caveat the corpus earns by being honest: there's no note here that studies the reversal curse by name. What the collection does hold is a cluster of findings about directional, non-reversible commitment — and read laterally, they make a fairly pointed case that the reversal curse is one symptom of a wider architectural posture rather than a quirk of memorization.
The clearest adjacent evidence is on backtracking. Frontier reasoning models hit a hard ceiling — 20-23% — on constraint satisfaction problems that require genuinely revisiting and undoing earlier choices Can reasoning models actually sustain long-chain reflection?. That's the same shape as the reversal curse: a commitment laid down in one direction is expensive or impossible to traverse the other way. The model can narrate reflection fluently but can't actually reverse course through the solution. If you believe a forward-only architecture struggles to run inference backward, the reversal curse stops looking like a storage bug and starts looking like a directionality bug.
The deeper framing comes from the critique of chain-of-thought as constrained imitation rather than abstract inference Why does chain-of-thought reasoning fail in predictable ways?. The argument there is that models pattern-match the *structure* of reasoning in the direction they saw it, and fail in distribution-bounded ways — structural coherence matters more to them than content-level truth. A symmetric fact like 'A is B = B is A' is abstract inference; pattern-matching a seen direction is not. So both failures trace to the same place: the model learned a forward mapping, not a reversible relation. You can even see the forward-commitment posture in how reasoning goes wrong tactically — models lock onto and abandon paths in one sweep rather than holding both directions open Do reasoning models switch between ideas too frequently?.
There's a quieter structural clue in how rollouts are organized: trajectories branch forward from a *shared prefix* Can shared-prefix trees reduce redundancy in agent rollouts?. The prefix is fixed and inherited; divergence only ever happens downstream, never upstream. That's the generation-time mirror of the reversal curse's training-time asymmetry — commitment flows one way through the sequence by construction.
The thing you might not have known you wanted to know: the corpus implies the reversal curse isn't best 'fixed' by feeding both orderings, but understood as the surface reading of a model that learns directional mappings and cannot natively run them in reverse — the same limitation that caps backtracking and makes chain-of-thought a forward imitation. If you want to chase the strongest version of that claim, the constraint-satisfaction ceiling and the constrained-imitation critique are the two doorways.
Sources 4 notes
DeepSeek-R1 and o1-preview achieve only 20-23.6% exact match on 850 constraint satisfaction problems requiring genuine backtracking. This ceiling reveals that reflective reasoning fluency does not translate to actual problem-solving competence on unfamiliar instance structures.
CoT guides models to pattern-match reasoning structure rather than perform genuine inference. This explains distribution-bounded failures, why structural coherence matters more than content correctness, and why performance optimizes against interpretability.
o1-like models frequently abandon reasoning paths mid-exploration, wasting tokens on incomplete approaches. A decoding-only penalty on thought-transition tokens (TIP strategy) discourages switching, improving accuracy on challenging math without model fine-tuning.
Tree-structured rollouts that branch from shared prefixes produce more distinct trajectories within a fixed token budget than independent chain sampling. This improves advantage estimation statistics and enables longer-horizon tasks within the same compute constraint.