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ARXIV:2603.21877 · LLM REASONING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.21877LLM REASONINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEXinyu Lu · Kaiqi Zhang · Jinglin Yang · Boxi Cao · Yaojie Lu · Hongyu Lin · +3 at arXiv
Optimize LLM reasoning by jointly evolving prompts and policies to efficiently learn from challenging examples.
Opportunity summary
Pain Optimize LLM reasoning by jointly evolving prompts and policies to efficiently learn from challenging examples.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Optimize LLM reasoning by jointly evolving prompts and policies to efficiently learn from challenging examples. However, vanilla RLVR suffers from inefficient exploration, particularly when confronting "hard samples" that yield nearzero success rates.
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a powerful paradigm for enhancing the reasoning capabilities of Large Language Models (LLMs). However, vanilla RLVR suffers from inefficient exploration, particularly when confronting "hard samples"…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In such scenarios, the reliance on sparse outcome rewards typically results in zero-advantage estimates, effectively starving the model of supervision signals despite the high…
LLM Reasoning moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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Optimize LLM reasoning by jointly evolving prompts and policies to efficiently learn from challenging examples.
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Paper Pack
10.48550/arXiv.2603.21877Optimize LLM reasoning by jointly evolving prompts and policies to efficiently learn from challenging examples.
Abstract
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a powerful paradigm for enhancing the reasoning capabilities of Large Language Models (LLMs). However, vanilla RLVR suffers from inefficient exploration, particularly when confronting "hard samples" that yield nearzero success rates. In such scenarios, the reliance on sparse outcome rewards typically results in zero-advantage estimates, effectively starving the model of supervision signals despite the high informational value of these instances. To address this, we propose P^2O, a novel framework that synergizes Prompt Optimization with Policy Optimization. P^2O identifies hard samples during training iterations and leverages the GeneticPareto (GEPA) prompt optimization algorithm to evolve prompt templates that guide the model toward discovering successful trajectories. Crucially, unlike traditional prompt engineering methods that rely on input augmentation, P^2O distills the reasoning gains induced by these optimized prompts directly into the model parameters. This mechanism provides denser positive supervision signals for hard samples and accelerates convergence. Extensive experiments demonstrate that P^2O not only achieves superior performance on in-distribution datasets but also exhibits strong generalization, yielding substantial improvements on out-of-distribution benchmarks (+4.7% avg.).
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Proof status
unverified0 refs; 0 sources; 17% coverage.
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Dimensions overall score 7.0
PROBLEM
Optimize LLM reasoning by jointly evolving prompts and policies to efficiently learn from challenging examples. However, vanilla RLVR suffers from inefficient exploration, particularly when confronting "hard samples" that yield nearzero success rates.
METHOD
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a powerful paradigm for enhancing the reasoning capabilities of Large Language Models (LLMs). However, vanilla RLVR suffers from inefficient exploration, particularly when confronting "hard samples" that yield...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In such scenarios, the reliance on sparse outcome rewards typically results in zero-advantage estimates, effectively starving the model of supervision signals despite the high informational value of these...
WHY NOW
LLM Reasoning moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
Optimize LLM reasoning by jointly evolving prompts and policies to efficiently learn from challenging examples. However, vanilla RLVR suffers from inefficient exploration, particularly when confronting "hard samples" that yield nearzero success rates.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a powerful paradigm for enhancing the reasoning capabilities of Large Language Models (LLMs). However, vanilla RLVR suffers from inefficient exploration, particularly when confronting "hard samples" that yield nearzero success rates.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In such scenarios, the reliance on sparse outcome rewards typically results in zero-advantage estimates, effectively starving the model of supervision signals despite the high informational value of these instances. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Reasoning moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Optimize LLM reasoning by jointly evolving prompts and policies to efficiently learn from challenging examples.
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LLM Reasoning
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