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ARXIV:2604.04767 · AI-DRIVEN CURRICULUM LEARNING · SUBMITTED 07 APR · 20:11 UTC · FRESHNESS UNKNOWN
ARXIV:2604.04767AI-DRIVEN CURRICULUM LEARNINGSUBMITTED 07 APR · 20:11 UTCFRESHNESS UNKNOWNJustin Chih-Yao Chen · Archiki Prasad · Zaid Khan · Joykirat Singh · Runchu Tian · Elias Stengel-Eskin · +1 at arXiv
Cog-DRIFT transforms hard reasoning problems into learnable formats for AI, enhancing LLM capabilities.
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Pain Cog-DRIFT transforms hard reasoning problems into learnable formats for AI, enhancing LLM capabilities.
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Cog-DRIFT transforms hard reasoning problems into learnable formats for AI, enhancing LLM capabilities. We propose a simple yet effective solution based on task reformulation.
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…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. These reformulations span a spectrum from discriminative to generative tasks, which we exploit to bootstrap learning: models first learn from structured, easier formats, and…
AI-Driven Curriculum Learning moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
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Cog-DRIFT transforms hard reasoning problems into learnable formats for AI, enhancing LLM capabilities.
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10.48550/arXiv.2604.04767Cog-DRIFT transforms hard reasoning problems into learnable formats for AI, enhancing LLM capabilities.
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.
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PROBLEM
Cog-DRIFT transforms hard reasoning problems into learnable formats for AI, enhancing LLM capabilities. We propose a simple yet effective solution based on task reformulation.
METHOD
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...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. 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...
WHY NOW
AI-Driven Curriculum Learning moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
Cog-DRIFT transforms hard reasoning problems into learnable formats for AI, enhancing LLM capabilities. We propose a simple yet effective solution based on task reformulation.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 8.0/10 on the public viability pass. 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. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
AI-Driven Curriculum Learning moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Cog-DRIFT transforms hard reasoning problems into learnable formats for AI, enhancing LLM capabilities.
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