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ARXIV:2604.02288 · LLM TRAINING · SUBMITTED 03 APR · 20:50 UTC · FRESHNESS STALE
ARXIV:2604.02288LLM TRAININGSUBMITTED 03 APR · 20:50 UTCFRESHNESS STALEGengsheng Li · Tianyu Yang · Junfeng Fang · Mingyang Song · Mao Zheng · Haiyun Guo · +3 at arXiv
A unified reinforcement learning framework for large language models that improves training stability and efficiency by intelligently routing samples to different optimization strategies.
Opportunity summary
Pain A unified reinforcement learning framework for large language models that improves training stability and efficiency by intelligently routing samples to different optimization strategies.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
A unified reinforcement learning framework for large language models that improves training stability and efficiency by intelligently routing samples to different optimization strategies. While Group Relative Policy Optimization (GRPO) is widely adopted, its coarse…
Reinforcement learning with verifiable rewards (RLVR) has become a standard paradigm for post-training large language models. While Group Relative Policy Optimization (GRPO) is widely adopted, its coarse credit assignment uniformly penalizes failed rollouts, lacking…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Evaluated across five benchmarks and two model scales, SRPO achieves both the rapid early improvement of SDPO and the long-horizon stability of GRPO. Code…
LLM Training moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A unified reinforcement learning framework for large language models that improves training stability and efficiency by intelligently routing samples to different optimization strategies.
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10.48550/arXiv.2604.02288A unified reinforcement learning framework for large language models that improves training stability and efficiency by intelligently routing samples to different optimization strategies.
Abstract
Reinforcement learning with verifiable rewards (RLVR) has become a standard paradigm for post-training large language models. While Group Relative Policy Optimization (GRPO) is widely adopted, its coarse credit assignment uniformly penalizes failed rollouts, lacking the token-level focus needed to efficiently address specific deviations. Self-Distillation Policy Optimization (SDPO) addresses this by providing denser, more targeted logit-level supervision that facilitates rapid early improvement, yet it frequently collapses during prolonged training. We trace this late-stage instability to two intrinsic flaws: self-distillation on already-correct samples introduces optimization ambiguity, and the self-teacher's signal reliability progressively degrades. To resolve these issues, we propose Sample-Routed Policy Optimization (SRPO), a unified on-policy framework that routes correct samples to GRPO's reward-aligned reinforcement and failed samples to SDPO's targeted logit-level correction. SRPO further incorporates an entropy-aware dynamic weighting mechanism to suppress high-entropy, unreliable distillation targets while emphasizing confident ones. Evaluated across five benchmarks and two model scales, SRPO achieves both the rapid early improvement of SDPO and the long-horizon stability of GRPO. It consistently surpasses the peak performance of both baselines, raising the five-benchmark average on Qwen3-8B by 3.4% over GRPO and 6.3% over SDPO, while simultaneously yielding moderate response lengths and lowering per-step compute cost by up to 17.2%.
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Dimensions overall score 7.0
PROBLEM
A unified reinforcement learning framework for large language models that improves training stability and efficiency by intelligently routing samples to different optimization strategies. While Group Relative Policy Optimization (GRPO) is widely adopted, its coarse credit assign...
METHOD
Reinforcement learning with verifiable rewards (RLVR) has become a standard paradigm for post-training large language models. While Group Relative Policy Optimization (GRPO) is widely adopted, its coarse credit assignment uniformly penalizes failed rollouts, lacking the token-le...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Evaluated across five benchmarks and two model scales, SRPO achieves both the rapid early improvement of SDPO and the long-horizon stability of GRPO. Code availability is flagged in the production record;...
WHY NOW
LLM Training moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
lowering per-step compute cost by up to 17.2%
Directly stated in abstract with specific numeric evidence
partial
simultaneously yielding moderate response lengths
Directly stated in abstract but without specific length metrics
partial
its coarse credit assignment uniformly penalizes failed rollouts, lacking the token-level focus needed to efficiently address specific deviations
Directly stated in abstract as a limitation of GRPO
partial
we trace this late-stage instability to two intrinsic flaws: self-distillation on already-correct samples introduces optimization ambiguity, and the self-teacher's signal reliability progressively degrades
Directly stated in abstract as traced causes of SDPO's late-stage instability
partial
routes correct samples to GRPO's reward-aligned reinforcement and failed samples to SDPO's targeted logit-level correction
Directly stated in abstract as the core mechanism of the proposed method
partial
SRPO further incorporates an entropy-aware dynamic weighting mechanism to suppress high-entropy, unreliable distillation targets while emphasizing confident ones
Directly stated in abstract as a key component of the proposed method
partial
SRPO achieves both the rapid early improvement of SDPO and the long-horizon stability of GRPO
Directly stated in abstract as a key result, though specific metrics would strengthen confidence
partial
raising the five-benchmark average on Qwen3-8B by 3.4% over GRPO and 6.3% over SDPO
Directly stated in abstract with specific numeric evidence
partial
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A unified reinforcement learning framework for large language models that improves training stability and efficiency by intelligently routing samples to different optimization strategies.
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