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ARXIV:2604.08905 · LLM AGENTS · SUBMITTED 13 APR · 20:24 UTC · FRESHNESS STALE
ARXIV:2604.08905LLM AGENTSSUBMITTED 13 APR · 20:24 UTCFRESHNESS STALEJinghan Zhang · Fengran Mo · Tharindu Cyril Weerasooriya · Ruimin Dai · Xiaoyan Han · Yanjie Fu · +2 at arXiv
A reinforcement learning framework that optimizes LLM reasoning by incorporating stability metrics like autocorrelation and path efficiency.
Opportunity summary
Pain A reinforcement learning framework that optimizes LLM reasoning by incorporating stability metrics like autocorrelation and path efficiency.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A reinforcement learning framework that optimizes LLM reasoning by incorporating stability metrics like autocorrelation and path efficiency. Existing RL policy optimization frameworks rely on final-answer correctness as feedback signals and rarely capture the internal…
Reinforcement learning (RL) is effective in enhancing the accuracy of large language models in complex reasoning tasks. Existing RL policy optimization frameworks rely on final-answer correctness as feedback signals and rarely capture the internal…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments on four reasoning benchmarks show that StaRPO consistently outperforms compared baselines and can enhance both final-answer accuracy and logical stability. Code availability is…
LLM Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A reinforcement learning framework that optimizes LLM reasoning by incorporating stability metrics like autocorrelation and path efficiency.
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10.48550/arXiv.2604.08905A reinforcement learning framework that optimizes LLM reasoning by incorporating stability metrics like autocorrelation and path efficiency.
Abstract
Reinforcement learning (RL) is effective in enhancing the accuracy of large language models in complex reasoning tasks. Existing RL policy optimization frameworks rely on final-answer correctness as feedback signals and rarely capture the internal logical structure of the reasoning process. Consequently, the models would generate fluent and semantically relevant responses but logically inconsistent, structurally erratic, or redundant. To this end, we propose StaRPO, a stability-augmented reinforcement learning framework that explicitly incorporates reasoning stability into the optimization objective. Our StaRPO decomposes stability into two computable lightweight metrics: the Autocorrelation Function (ACF) to evaluate local step-to-step coherence, and Path Efficiency (PE) to evaluate global goal-directedness of the reasoning trajectory. These stability rewards are combined with task rewards to provide complementary and process-aware feedback. We validate the effectiveness of using ACF and PE rewards by showing their correlation with logic errors on two backbone models. Experiments on four reasoning benchmarks show that StaRPO consistently outperforms compared baselines and can enhance both final-answer accuracy and logical stability.
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PROBLEM
A reinforcement learning framework that optimizes LLM reasoning by incorporating stability metrics like autocorrelation and path efficiency. Existing RL policy optimization frameworks rely on final-answer correctness as feedback signals and rarely capture the internal logical st...
METHOD
Reinforcement learning (RL) is effective in enhancing the accuracy of large language models in complex reasoning tasks. Existing RL policy optimization frameworks rely on final-answer correctness as feedback signals and rarely capture the internal logical structure of the reason...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments on four reasoning benchmarks show that StaRPO consistently outperforms compared baselines and can enhance both final-answer accuracy and logical stability. Code availability is flagged in the...
WHY NOW
LLM Agents 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.
A reinforcement learning framework that optimizes LLM reasoning by incorporating stability metrics like autocorrelation and path efficiency. Existing RL policy optimization frameworks rely on final-answer correctness as feedback signals and rarely capture the internal logical structure of the reasoning process.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Reinforcement learning (RL) is effective in enhancing the accuracy of large language models in complex reasoning tasks. Existing RL policy optimization frameworks rely on final-answer correctness as feedback signals and rarely capture the internal logical structure of the reasoning process.
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. Experiments on four reasoning benchmarks show that StaRPO consistently outperforms compared baselines and can enhance both final-answer accuracy and logical stability. 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 Agents 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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A reinforcement learning framework that optimizes LLM reasoning by incorporating stability metrics like autocorrelation and path efficiency.
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