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ARXIV:2602.03309 · LLM TRAINING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2602.03309LLM TRAININGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Develop an entropy-gated framework for optimizing hybrid training in language models to improve mathematical reasoning performance.
Opportunity summary
Pain Develop an entropy-gated framework for optimizing hybrid training in language models to improve mathematical reasoning performance.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Develop an entropy-gated framework for optimizing hybrid training in language models to improve mathematical reasoning performance. We propose Entropy Gated Selective Policy Optimization (EGSPO), a three stage framework that extends sample level mixing with…
Hybrid training methods for large language models combine supervised fine tuning (SFT) on expert demonstrations with reinforcement learning (RL) on model rollouts, typically at the sample level. We propose Entropy Gated Selective Policy Optimization…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. EGSPO achieves consistent improvements on mathematical reasoning benchmarks, with gains of 3.8 percent on AIME and 2.9 percent on MATH over the CHORD phi…
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Develop an entropy-gated framework for optimizing hybrid training in language models to improve mathematical reasoning performance.
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Paper Pack
10.48550/arXiv.2602.03309Develop an entropy-gated framework for optimizing hybrid training in language models to improve mathematical reasoning performance.
Abstract
Hybrid training methods for large language models combine supervised fine tuning (SFT) on expert demonstrations with reinforcement learning (RL) on model rollouts, typically at the sample level. We propose Entropy Gated Selective Policy Optimization (EGSPO), a three stage framework that extends sample level mixing with token level gradient modulation. Stage 1, SFT expert learning, establishes a reliable warm up policy using expert demonstrations with a pure SFT loss. Stage 2, RL rollout generation, samples trajectories from the current policy and computes per token predictive entropy. Stage 3, the EGSPO mechanism, applies entropy gated gradient allocation: a predictive entropy module routes high entropy tokens to full PPO updates to encourage exploration, and low entropy tokens to attenuated PPO updates to reduce variance and preserve knowledge. Critically, both branches incorporate the advantage function A_t, ensuring that incorrect trajectories receive consistent negative learning signals and preventing reinforcement of confident errors. EGSPO achieves consistent improvements on mathematical reasoning benchmarks, with gains of 3.8 percent on AIME and 2.9 percent on MATH over the CHORD phi baseline, while incurring only 3.4 percent additional computational overhead.
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unverified0 refs; 0 sources; 17% coverage.
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PROBLEM
Develop an entropy-gated framework for optimizing hybrid training in language models to improve mathematical reasoning performance. We propose Entropy Gated Selective Policy Optimization (EGSPO), a three stage framework that extends sample level mixing with token level gradient...
METHOD
Hybrid training methods for large language models combine supervised fine tuning (SFT) on expert demonstrations with reinforcement learning (RL) on model rollouts, typically at the sample level. We propose Entropy Gated Selective Policy Optimization (EGSPO), a three stage framew...
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. EGSPO achieves consistent improvements on mathematical reasoning benchmarks, with gains of 3.8 percent on AIME and 2.9 percent on MATH over the CHORD phi baseline, while incurring only 3.4 percent additio...
WHY NOW
LLM Training moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Develop an entropy-gated framework for optimizing hybrid training in language models to improve mathematical reasoning performance. We propose Entropy Gated Selective Policy Optimization (EGSPO), a three stage framework that extends sample level mixing with token level gradient modulation.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Hybrid training methods for large language models combine supervised fine tuning (SFT) on expert demonstrations with reinforcement learning (RL) on model rollouts, typically at the sample level. We propose Entropy Gated Selective Policy Optimization (EGSPO), a three stage framework that extends sample level mixing with token level gradient modulation.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. EGSPO achieves consistent improvements on mathematical reasoning benchmarks, with gains of 3.8 percent on AIME and 2.9 percent on MATH over the CHORD phi baseline, while incurring only 3.4 percent additional computational overhead.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Training moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Develop an entropy-gated framework for optimizing hybrid training in language models to improve mathematical reasoning performance.
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