6arXiv cs.CL (Computation and Language)·
Experiential Learning proposes LLM-as-a-Coach to replace scalar RL rewards with textual feedback distillation
Researchers introduce Experiential Learning (EL), a post-training method that replaces scalar RL rewards with rich textual feedback from an LLM-as-a-Coach, which distills per-response assessments into transferable experiential knowledge used to condition a teacher model and update the policy via context distillation. Evaluated across two policy families with both self-feedback and proprietary model feedback, EL consistently outperforms rubric-based RL on open-ended tasks and shows better out-of-distribution generalization while mitigating reward hacking. The work targets a known weakness of RLHF-style training on non-verifiable tasks where scalar rewards lose fine-grained preference signal.
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ExpRL: RL-based mid-training using human QA data as reward scaffolds for LLM reasoning
ExpRL proposes an automated approach to LLM mid-training that replaces manually curated reasoning traces with large corpora of human-written QA data used as reward scaffolds rather than imitation targets. Reference solutions are hidden from the policy and used only to construct problem-specific grading rubrics, enabling dense process-level rewards that reinforce partial progress and intermediate reasoning steps. On challenging math reasoning benchmarks, ExpRL outperforms SFT, sparse-reward GRPO, and self-distillation as an RL initialization strategy, with additional mixed-domain experiments suggesting broader applicability.
Reinforcement Learning with Metacognitive Feedback (RLMF) improves LLM calibration and uncertainty expression
Researchers introduce Reinforcement Learning with Metacognitive Feedback (RLMF), a training paradigm that refines preference optimization using a model's self-judgments of its own performance quality. The method is applied to faithful calibration — aligning a model's expressed confidence with its intrinsic uncertainty — and achieves state-of-the-art results across diverse tasks while outperforming standard RL by up to 63%. A companion technique, metacognitive data selection, uses similar self-judgments to identify high-value training examples, outperforming naive active learning baselines. The work positions metacognitive performance as a novel and effective RL signal for improving LLM reliability and alignment.
Progress Advantage: Annotation-Free Step-Level Scoring for LLM Agents via RL Post-Training
Researchers introduce 'progress advantage,' a method that derives implicit step-level reward signals for LLM agents directly from the log-probability ratio between an RL-trained policy and its reference policy, without requiring dedicated process reward model training. The approach is shown to recover the optimal advantage function under a general stochastic MDP formulation, making it annotation-free and domain-agnostic. Validated across five benchmarks and four model families on tasks including test-time scaling, uncertainty quantification, and failure attribution, it outperforms confidence-based baselines and even dedicated trained reward models. The result is practically significant because building process reward models for agentic settings is currently a major bottleneck.
GR2: Generative Reasoning Re-Ranker applies RL with verifiable rewards to industrial-scale LLM recommendation
GR2 (Generative Reasoning Re-Ranker) is a new framework that applies reinforcement learning with verifiable rewards to the re-ranking stage of industrial recommendation systems, a step largely overlooked by prior LLM-based recommendation research. The system combines semantic ID mid-training, reasoning-trace distillation from a stronger teacher model, and purpose-built RL rewards, plus a context compressor and On-Policy Distillation to make it viable at scale. Deployed on industrial traffic, GR2 achieves +18.7% R@1 and +9.6% N@3 over legacy baselines. The paper also identifies a critical reward-hacking failure mode where LLMs exploit position bias or preserve input order, motivating conditional verifiable rewards.
DistIL: Distributional DAgger for RL from Rich Feedback beyond single-bit rewards
A new arXiv preprint introduces DistIL, a distributional variant of the DAgger imitation learning algorithm designed to exploit rich feedback signals (execution traces, tool outputs, expert corrections) rather than the single-bit correctness reward used in standard RLVR. The method uses a forward cross-entropy objective that provides monotonic policy improvement guarantees, unlike reverse KL or Jensen-Shannon divergence objectives used in prior self-distillation approaches. Empirically, DistIL outperforms RLVR and self-distillation baselines on scientific reasoning, coding, and hard math benchmarks.
Rubric-Conditioned Self-Distillation: structured feedback for reasoning model post-training
A new arXiv preprint proposes Rubric-Conditioned Self-Distillation (RCSD), a post-training framework that replaces scalar reward signals and noisy chain-of-thought annotations with structured rubrics for fine-grained credit assignment. The method conditions a teacher model on criterion-level rubrics to provide token-level guidance on the student's own sampled trajectories, avoiding reliance on a single reference rationale. Evaluated on science reasoning benchmarks, RCSD outperforms GRPO by 1.0 points and OPSD by 0.9 points on average.
Turing-RL: Reinforcement learning with Turing-Test-based rewards for user simulator training
Researchers propose Turing-RL, a method for training LLM-based user simulators using a discriminative reward signal that scores how indistinguishable generated responses are from real user responses, rather than matching a single ground-truth output. An LLM judge evaluates indistinguishability given the user's history, and the simulator is trained via RL to maximize this reward. Evaluated on conversational chat and Reddit forum discussion domains, Turing-RL outperforms log-probability and similarity-reward baselines on both LLM and human evaluation metrics. The work has implications for agent assistant training, personalization system evaluation, and social science research.
LongTraceRL: Reinforcement Learning for Long-Context Reasoning via Search Agent Trajectories and Rubric Rewards
LongTraceRL is a new RL training framework for improving long-context reasoning in LLMs, addressing limitations of existing RLVR methods. It constructs challenging training data using multi-hop questions from knowledge graph random walks and tiered distractors derived from search agent trajectories (high-confusability: read but uncited; low-confusability: seen but unopened). A rubric reward provides entity-level process supervision along reasoning chains, applied only to correct responses to prevent reward hacking. Experiments across three LLMs (4B–30B parameters) on five long-context benchmarks show consistent improvements over strong baselines.