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Recent years have witnessed the rapid adoption of reinforcement learning (RL) in large language model (LLM) post-training, with substantial gains in mathematical reasoning and code generation. In practical systems, however, policy updates and differences between rollout and training engines can make sampled responses o...
TCPO casts credit assignment as score-to-credit conversion and constructs turn-level advantages through reference-based comparisons and achieves the best or tied-best best-turn Pass@8 on Qwen3-4B and DeepSeek-R1-Distill-Llama-8B, reduces turns to success, and improves multi-turn agent performance.
This work introduces LEAP (Lean Environment-Feedback via Adaptive Pruning), a scalable and computationally efficient multi-turn RL framework optimized for low-level hardware accelerator alignment and proposes a Rank-Based Reward formulation, establishing a practical paradigm for low-level code RL.
Strong students can be obtained not by selecting a single better teacher, but by deliberately constructing and compressing a complementary teacher union, in an expand-then-compress framework that couples teacher construction with multi-teacher policy distillation.
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