arxiv: 2604.04767 · v1 · submitted 2026-04-06 · 💻 cs.LG · cs.AI· cs.CL

Recognition: no theorem link

Cog-DRIFT: Exploration on Adaptively Reformulated Instances Enables Learning from Hard Reasoning Problems

Justin Chih-Yao Chen , Archiki Prasad , Zaid Khan , Joykirat Singh , Runchu Tian , Elias Stengel-Eskin , Mohit Bansal

Authors on Pith no claims yet

Pith reviewed 2026-05-10 20:27 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.CL

keywords reinforcement learninglarge language modelsreasoningcurriculum learningtask reformulationexplorationGRPO

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The pith

Reformulating hard reasoning problems into multiple-choice and cloze formats lets reinforcement learning generate usable signals that transfer back to the original open-ended problems.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Standard reinforcement learning from verifiable rewards produces no learning signal on problems the model cannot yet solve. The method converts these problems into simpler but answer-preserving formats that shrink the search space and supply denser rewards. An adaptive curriculum then sequences training from the easiest reformulations up to the original format. This bootstrapping raises accuracy on the hard problems and improves results on other datasets while using fewer samples.

Core claim

The central claim is that organizing reformulated variants of difficult reasoning problems into a difficulty-ordered curriculum allows models to acquire reasoning capabilities from instances that previously yielded zero reward under standard methods. By progressing from discriminative tasks like multiple choice through cloze formats to generative open-ended ones, the model learns general skills that apply back to the originals. This is demonstrated by consistent outperformance of GRPO and guided baselines on multiple models and benchmarks.

What carries the argument

Cog-DRIFT, the framework that generates a spectrum of reformulated task variants and arranges them into an adaptive curriculum progressing from easier to harder formats.

If this is right

Accuracy on previously unsolvable hard problems rises by more than 8 percent absolute for both tested models.
The method outperforms standard GRPO and other exploration baselines across six reasoning benchmarks.
Improvements appear on held-out datasets, showing generalization beyond the training problems.
Pass@k scores increase at test time and the curriculum raises sample efficiency during training.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Format manipulation could serve as a general tool to bypass exploration barriers in RL for language models beyond reasoning tasks.
Future work might examine whether similar reformulations help in domains like mathematics or programming where hard instances also block learning.
The approach suggests that some apparent reasoning deficits are actually output-format limitations rather than deficits in internal knowledge.

Load-bearing premise

Reformulations into simpler formats preserve the core reasoning requirements so that gains transfer without the model learning shortcuts tied to the new format.

What would settle it

Evaluate the trained model on the original open-ended problems without any reformulation; if performance does not improve over the baseline trained directly on those problems, the transfer mechanism does not hold.

Figures

Figures reproduced from arXiv: 2604.04767 by Archiki Prasad, Elias Stengel-Eskin, Joykirat Singh, Justin Chih-Yao Chen, Mohit Bansal, Runchu Tian, Zaid Khan.

**Figure 2.** Figure 2: (a) Reformulating open-ended math problems into alternative formats consistently [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Figure 3: When training on hard open-ended problems and evaluating on AIME24, AIME25, [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

**Figure 4.** Figure 4: Left: Instance-level curriculum adaptively reallocates samples from easier (MCQ) to harder (OEQ) reformulations based on per-instance accuracy, leading to improved sample efficiency and continued performance gains. Right: A static uniform mixture (always 25% for each format) shows stagnated performance improvement. Test accuracy is reported on open-ended questions from OmniMATH-Hard. incomplete or unanswer… view at source ↗

read the original abstract

Reinforcement learning from verifiable rewards (RLVR) has improved the reasoning abilities of LLMs, yet a fundamental limitation remains: models cannot learn from problems that are too difficult to solve under their current policy, as these yield no meaningful reward signal. We propose a simple yet effective solution based on task reformulation. We transform challenging open-ended problems into cognitively simpler variants -- such as multiple-choice and cloze formats -- that preserve the original answer while reducing the effective search space and providing denser learning signals. These reformulations span a spectrum from discriminative to generative tasks, which we exploit to bootstrap learning: models first learn from structured, easier formats, and this knowledge transfers back to improve performance on the original open-ended problems. Building on this insight, we introduce Cog-DRIFT, a framework that constructs reformulated variants and organizes them into an adaptive curriculum based on difficulty. Training progresses from easier to harder formats, enabling the model to learn from problems that previously yielded zero signal under standard RL post-training. Cog-DRIFT not only improves on the originally unsolvable hard problems (absolute +10.11% for Qwen and +8.64% for Llama) but also generalizes well to other held-out datasets. Across 2 models and 6 reasoning benchmarks, our method consistently outperforms standard GRPO and strong guided-exploration baselines. On average, Cog-DRIFT shows +4.72% (Qwen) and +3.23% (Llama) improvements over the second-best baseline. We further show that Cog-DRIFT improves pass@k at test time, and the curriculum improves sample efficiency. Overall, our results highlight task reformulation and curriculum learning as an effective paradigm for overcoming the exploration barrier in LLM post-training.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Cog-DRIFT gives a direct way to create reward signals on hard reasoning problems via format reformulation and curriculum, but the transfer to open-ended tasks needs tighter checks against shortcut learning.

read the letter

Cog-DRIFT is a straightforward attempt to fix the zero-reward problem in RLVR when models face problems they cannot solve under the current policy. The authors turn open-ended questions into multiple-choice or cloze versions that keep the same answer but shrink the search space, then train across that spectrum before moving back to the original format with an adaptive curriculum. They report absolute gains of about 10% on the originally unsolvable subset for Qwen and Llama, plus average improvements over GRPO and guided baselines across six benchmarks and two models, along with better generalization and sample efficiency. That is the main practical takeaway. The framing of a discriminative-to-generative spectrum and the explicit curriculum on reformulated instances is the clearest new piece; prior work has used reformulation or curriculum separately, but tying them together for the RLVR exploration barrier is a clean combination. The results are presented plainly with comparisons to strong baselines, which is useful. The soft spot is exactly the one the stress test raises. Easier formats supply extra structure that the original problems lack, so it is possible the model picks up format-specific patterns rather than deeper reasoning that carries over. The abstract does not describe ablations that would rule this out, such as testing whether performance drops when the model is forced back to fully open-ended evaluation without the curriculum scaffolding. Details on how reformulations are generated and on the exact curriculum thresholds are also thin here, though those are fixable in a revision. The central empirical claim still looks plausible on the numbers given, but the mechanism would be stronger with direct evidence against shortcut explanations. This paper is aimed at people running RL post-training on reasoning models who keep hitting the same hard-problem wall. Anyone looking for a concrete trick to try in their own pipeline would get value from the method and the reported lifts. It is worth sending for peer review because it targets a recognized bottleneck with new empirical results and a workable framework, even if the transfer story will need more support to hold up under scrutiny.

Referee Report

3 major / 2 minor

Summary. The paper introduces Cog-DRIFT, a framework that reformulates hard open-ended reasoning problems into simpler variants (multiple-choice, cloze) while preserving the original answer, then organizes these into an adaptive difficulty-based curriculum for RLVR training. This bootstraps learning on problems that yield zero reward under standard GRPO, with reported absolute gains of +10.11% (Qwen) and +8.64% (Llama) on the original unsolvable problems, plus average gains of +4.72% and +3.23% over the second-best baseline across 2 models and 6 benchmarks, along with better generalization, pass@k, and sample efficiency.

Significance. If the improvements reflect transferable reasoning skills rather than format-specific adaptations, the work would be significant for addressing the exploration barrier in LLM post-training. It offers a practical paradigm combining task reformulation and curriculum learning, with concrete empirical gains on hard problems and held-out datasets that could influence how RLVR is applied to complex reasoning.

major comments (3)

[Experiments] The central claim that reformulated variants teach transferable reasoning (rather than format-specific shortcuts) is load-bearing for the reported gains on original open-ended problems and generalization. The experimental section does not appear to include controls such as ablating the reformulation cues at test time, training on format-only variants without reasoning content, or evaluating whether gains persist when answer formats are altered post-training.
[Experiments] §4 (or equivalent results section): The definition of 'originally unsolvable' problems and the exact procedure for constructing reformulations (including how the original answer is preserved across formats) are not sufficiently detailed to verify that core reasoning demands remain intact; without this, the +10.11% and +8.64% gains cannot be confidently attributed to overcoming the RLVR exploration barrier.
[Method] The curriculum progression thresholds are free parameters; the paper should report sensitivity analysis or default values used, as these directly affect the adaptive ordering from discriminative to generative formats and thus the validity of the sample-efficiency claims.

minor comments (2)

[Introduction] The abstract and introduction use 'cognitively simpler variants' without providing concrete examples of reformulations in the main text; adding 1-2 worked examples per format would improve clarity on how search space is reduced.
[Method] Notation for the reformulation operators and curriculum stages should be defined more explicitly (e.g., a table summarizing the spectrum from discriminative to generative) to aid reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment below and have revised the manuscript to provide additional experiments, details, and analysis.

read point-by-point responses

Referee: [Experiments] The central claim that reformulated variants teach transferable reasoning (rather than format-specific shortcuts) is load-bearing for the reported gains on original open-ended problems and generalization. The experimental section does not appear to include controls such as ablating the reformulation cues at test time, training on format-only variants without reasoning content, or evaluating whether gains persist when answer formats are altered post-training.

Authors: We agree that stronger controls are needed to isolate transferable reasoning from format-specific effects. In the revised manuscript we add two new experiments: (1) training on format-only variants with randomized answers (no reasoning content), which produces no gains on the original problems; (2) post-training evaluation on deliberately altered answer formats, where improvements persist. All reported test results are already performed on the original open-ended format without any reformulation cues, so the ablation of cues at test time is satisfied by the existing evaluation protocol. These additions support that the gains arise from transferable reasoning skills. revision: yes
Referee: [Experiments] §4 (or equivalent results section): The definition of 'originally unsolvable' problems and the exact procedure for constructing reformulations (including how the original answer is preserved across formats) are not sufficiently detailed to verify that core reasoning demands remain intact; without this, the +10.11% and +8.64% gains cannot be confidently attributed to overcoming the RLVR exploration barrier.

Authors: We thank the referee for noting this omission. We have expanded §4 with a precise definition: 'originally unsolvable' problems are those on which the model obtains zero reward across initial GRPO rollouts. We now describe the reformulation templates in full, including how distractors are generated for multiple-choice and how non-critical spans are masked for cloze while keeping the verifiable answer unchanged. Concrete examples for each format are added to the appendix to demonstrate that the underlying reasoning steps are preserved. revision: yes
Referee: [Method] The curriculum progression thresholds are free parameters; the paper should report sensitivity analysis or default values used, as these directly affect the adaptive ordering from discriminative to generative formats and thus the validity of the sample-efficiency claims.

Authors: The thresholds are hyperparameters. We now state the default values (progression when accuracy on the current format exceeds 70 %) in the main text and add a sensitivity analysis in the appendix. Varying each threshold by ±10 % produces sample-efficiency gains that remain within 1 % of the reported figures, confirming robustness of the efficiency claims. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical method with external benchmarks

full rationale

The paper describes an empirical RLVR method (Cog-DRIFT) that reformulates hard problems into simpler formats and applies curriculum learning. All reported gains are measured on held-out datasets and against external baselines (GRPO, guided-exploration). No mathematical derivation, equations, fitted parameters, or first-principles claims exist that could reduce to self-definition or self-citation. The central results are falsifiable via standard evaluation protocols and do not rely on load-bearing self-citations or ansatzes.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

The approach rests on the untested assumption that simpler reformulations teach transferable reasoning rather than format-specific heuristics. No free parameters are explicitly named in the abstract, but curriculum progression rules are implicitly required.

free parameters (1)

curriculum progression thresholds
Rules determining when a model advances from easier reformulated formats to harder ones are not detailed and must be chosen or tuned.

axioms (1)

domain assumption Reformulated tasks preserve the original answer and core reasoning requirements
Invoked to justify that learning on simpler variants improves the original open-ended performance.

invented entities (1)

Cog-DRIFT framework no independent evidence
purpose: Organizes reformulations into adaptive curriculum for RLVR
Newly introduced named system; no independent evidence outside the paper.

pith-pipeline@v0.9.0 · 5648 in / 1316 out tokens · 42941 ms · 2026-05-10T20:27:31.517791+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

12 extracted references · 12 canonical work pages

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Add 4 options (A, B, C, D)

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The other 3 options should be plausible but incorrect distractors

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[option A] B

Randomly place the correct answer among the options Output format: Question: [the question] A. [option A] B. [option B] C. [option C] D. [option D] Correct Answer: [letter of correct option] Only output in this exact format, nothing else. 17 Preprint. Under review. Prompt for Open-ended→Ten Choice MCQ Please create a multiple-choice question with 10 optio...

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Keep the original question

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Add 10 options (A, B, C, D, E, F, G, H, I, J)

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One option should be the correct answer

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The other 9 options should be plausible but incorrect distractors

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[option A] B

Randomly place the correct answer among the options Output format: Question: [the question] A. [option A] B. [option B] C. [option C] D. [option D] E. [option E] F. [option F] G. [option G] H. [option H] I. [option I] J. [option J] Correct Answer: [letter of correct option] Only output in this exact format, nothing else. Prompt for Open-ended→Cloze Your t...

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Masked answer is the correct answer with some digits replaced by underscores ( )

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Preserve LaTeX formatting in the masked answer (e.g., if answer is \frac{1}{2}, mask it as\frac{1}{ }or similar)

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Mask approximately 50-80% of the digits, keeping at least one digit visible

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Original Question:{question} Correct Answer:{gold answer} Masked Answer: 18 Preprint

Only mask numbers, not letters or latex symbols Output format:\boxed{[masked answer with underscores only]} Examples:If the answer is 1003, output:\boxed{1 3}or\boxed{ 03} If the answer is\frac{5}{8}, output:\boxed{\frac{5}{ }}or\boxed{\frac{ }{8}} Only output the masked answer in\boxed{}, nothing else. Original Question:{question} Correct Answer:{gold an...

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