Research category
Reward modeling, RLHF-style optimization, reasoning RL, agent RL, and VLA policy learning.
13 papers
This paper introduces layer contribution to measure how much of full-parameter RL improvement can be recovered by training each transformer layer independently. Across multiple models, RL algorithms, and tasks, it finds that gains consistently concentrate in a few middle layers and that single-layer training can match or surpass full-parameter training.
This paper introduces Selective Importance Sampling (SIS), a plug-in correction for off-policy LLM reinforcement learning that uses token-level rejection tests to treat accepted tokens as on-policy while retaining standard importance sampling for rejected tokens. SIS reduces the gap between token- and sequence-level gradient estimators with negligible overhead, improving performance and robustness across dense and MoE models.
This paper introduces SERPO, a test-time reinforcement learning framework for open-ended generation that co-evolves response evidence, query-specific rubrics, and policy parameters without labeled feedback, external reward models, or stronger judges. Probabilistic criterion scoring turns verdict-token likelihoods into rewards, enabling the actor and its self-generated evaluation criteria to improve in a closed loop.
This paper proposes Divergence Regularized Policy Optimization (DRPO), replacing DPPO's hard divergence mask with a smooth advantage-weighted quadratic regularizer that preserves its trust-region geometry. DRPO provides bounded continuous gradient weights and corrective signals beyond the trust-region boundary, improving LLM RL training stability and efficiency.
This paper formulates LLM reinforcement learning as constrained policy optimization, unifying existing algorithms through different constraint choices and exposing the roles of clipping, KL regularization, and trust regions. It derives Constraint-Infused Policy Optimization (CIPO), which improves reasoning performance and training stability across diverse tasks and model families.
BandPO projects trust regions defined by general divergence measures into dynamic, probability-aware clipping intervals, expanding the update margin for low-probability high-advantage actions. This principled replacement for fixed PPO-style clipping improves exploration and robustly mitigates entropy collapse across LLM reinforcement learning settings.
This paper introduces Soft Adaptive Policy Optimization (SAPO), replacing hard clipping in group-based LLM reinforcement learning with a temperature-controlled soft gate that continuously attenuates off-policy token updates. Its sequence-coherent, token-adaptive objective improves training stability, sample efficiency, and reasoning performance across text and Qwen3-VL models.
This paper introduces GSPO, a reinforcement learning algorithm for LLMs that replaces token-level importance ratios with sequence-level likelihood ratios and performs sequence-level clipping, rewarding, and optimization. GSPO improves training efficiency and performance over GRPO, stabilizes MoE RL training, and helps simplify large-scale RL infrastructure for Qwen3 models.
This paper introduces Decoupled Clip and Dynamic Sampling Policy Optimization (DAPO), an open large-scale reinforcement learning system for eliciting LLM reasoning. It releases the training recipe, code, dataset, and model weights, reaching 50 points on AIME 2024 with Qwen2.5-32B and improving reproducibility for large-scale LLM RL.
This paper identifies RLHF overoptimization as a consequence of distribution shift and uncertainty in learned preferences, then introduces Regularized Preference Optimization (RPO), which combines a preference optimization objective with an SFT loss that acts as an adversarial regularizer. RPO provides finite-sample guarantees and empirically improves alignment over DPO while reducing drift toward undesired responses.
This paper frames successful LLM alignment losses as human-aware losses that encode biases from prospect theory, then introduces KTO to optimize generation utility directly from binary desirable/undesirable feedback. KTO matches or exceeds preference-pair methods from 1B to 30B scales, highlighting how the best alignment loss depends on the setting's inductive biases.
This paper introduces Direct Preference Optimization (DPO), which reparameterizes the RLHF reward model so the optimal policy can be learned directly from preference data with a simple classification loss. DPO removes separate reward-model fitting and online reinforcement learning while matching or improving PPO-based RLHF with simpler, more stable training.
This paper introduces Proximal Policy Optimization (PPO), a family of policy-gradient methods that alternates environment sampling with multiple minibatch epochs on a surrogate objective. PPO retains key trust-region benefits while being simpler to implement and empirically balancing sample efficiency, performance, and wall-clock time.
9 papers
This paper finds that noisy teacher supervision is not the main source of on-policy distillation gains: suppressing student-sampled low-probability tokens with a fixed negative advantage achieves comparable performance. It introduces On-Policy Self-Adaptation (OPSA), a teacher- and reward-free method that scales negative advantages by token entropy to improve reasoning and generalization.
This paper identifies the unbounded log-ratio reward as the source of high-variance gradients and unstable training in sampled-token on-policy distillation. PowerOPD replaces it with a bounded, sign-consistent reward family derived from the Box-Cox power transformation, improving mathematical reasoning accuracy while reducing time and memory relative to full-vocabulary OPD.
This paper analyzes why on-policy self-distillation can hurt math reasoning, showing through pointwise mutual information that privileged context overemphasizes solution-implied tokens while suppressing deliberation tokens needed for search. It proposes AntiSD, which ascends rather than descends the self-distillation divergence with an entropy gate, reaching GRPO-level accuracy in 2 to 10x fewer steps and improving final accuracy by up to 11.5 points.
This paper proposes Draft-OPD, which adapts speculative draft models from RL-trained LLM traces through on-policy distillation without requiring expensive online generation for the draft model. It proves an equivalence between RL training and OPD-style distillation, reuses collected RL experience, and improves speculative decoding speed by up to 2.14x while preserving task performance.
This paper introduces OmniOPD, a logit-free on-policy distillation framework that replaces brittle token-level logit matching with Monte Carlo chunk rollouts scored by semantic similarity, enabling black-box teachers. A peak-entropy scheduler focuses verification on uncertain reasoning forks, while Bayesian smoothing and a base-model KL anchor stabilize training; it outperforms standard OPD by up to 28.64% on math.
This paper studies on-policy self-distillation for RLVR and shows that relying only on a privileged self-teacher can cause information leakage and unstable long-term training. It proposes RLSD, which uses self-distillation to estimate token-level update magnitudes while keeping RLVR's environment feedback as the reliable update direction.
This paper shows that on-policy distillation is a special case of dense KL-constrained RL, then proposes G-OPD with a flexible reference model and reward scaling factor. Its reward extrapolation variant, ExOPD, improves over standard OPD and can let students surpass domain teachers when merging RL-trained experts.
This paper introduces On-Policy Self-Distillation (OPSD), where a single LLM acts as teacher and student under different contexts: the teacher sees privileged verified reasoning traces while the student samples from the question-only policy. By matching per-token distributions over the student's on-policy rollouts, OPSD provides dense supervision without an external teacher and achieves stronger token efficiency than GRPO and off-policy distillation on math reasoning.
This paper introduces Self-Distillation Fine-Tuning (SDFT), an on-policy self-distillation method that learns from expert demonstrations by using a demonstration-conditioned model as its own teacher. It improves new-task acquisition while reducing catastrophic forgetting, enabling sequential accumulation of skills and knowledge without explicit reward functions.
7 papers
This paper frames reliable verification as the emerging bottleneck for coding agents and evaluates reward signals by scalability, faithfulness, and robustness. Through test, rubric, user-feedback, and automated-agent verifiers, it shows that targeted designs can curb reward hacking and argues that verification must co-evolve with increasingly capable generators.
This paper revisits value modeling for LLM reinforcement learning and argues that one-shot discriminative critics are limited by their expressiveness. It introduces Generative Actor-Critic (GenAC), whose critic reasons before estimating value and uses in-context conditioning to track the current actor, improving value approximation, ranking reliability, out-of-distribution generalization, and downstream RL performance.
This paper introduces E-GRM, which estimates uncertainty from the convergence of parallel model generations and triggers chain-of-thought reasoning only when needed. A lightweight discriminative scorer trained with a hybrid regression-ranking objective provides fine-grained reasoning-path rewards, reducing inference cost while improving accuracy.
This paper introduces MemReward, a graph-based experience memory framework that achieves 97.3% of Oracle performance on 3B and 96.6% on 1.5B models for reward prediction with limited labels, surpassing Oracle in out-of-domain tasks.
This paper studies unsupervised reward model scaling by learning preferences over web-corpus document prefixes and suffixes without human annotations. It reports consistent RewardBench gains across model backbones and shows downstream improvements in best-of-N selection and policy optimization.
This paper introduces RLVR on preference data for training Generative Reward Models, demonstrating that binary classification tasks make GRMs susceptible to guessing correct outcomes without sound critiques, and proposes a method to address this limitation.
This survey reviews Process Reward Models for evaluating and guiding LLM reasoning at the step or trajectory level rather than only judging final answers. It organizes the full loop of process data generation, PRM construction, and PRM use in test-time scaling and reinforcement learning across math, code, multimodal reasoning, robotics, and agents.
1 paper
KVPO aligns streaming autoregressive video generators with human preferences using an ODE-native online GRPO framework. It replaces noise-based exploration with causal-semantic routing of historical KV cache entries and optimizes a velocity-field surrogate policy based on Trajectory Velocity Energy.
2 papers
This paper introduces CapRL, the first RLVR framework for open-ended image captioning, which rewards captions by whether a vision-free language model can answer image questions using only the generated description. The resulting CapRL-3B model produces more informative and diverse captions, while its generated caption data improves large vision-language model pretraining across 12 benchmarks.
This paper introduces Visual Planning, a paradigm that performs step-by-step planning through sequences of images rather than text for vision-first spatial tasks. Its Visual Planning via Reinforcement Learning (VPRL) framework uses GRPO to post-train large vision models and improves planning on FrozenLake, Maze, and MiniBehavior.
10 papers
This paper scales reinforcement learning with verifiable rewards from a base model to one trillion parameters through a stable pipeline combining clipped importance sampling, training-inference ratio correction, and mixed-precision control. The resulting model improves sample efficiency and reasoning quality while spontaneously developing structured, self-verifying, and adaptive reasoning behaviors.
This paper presents MUPO, a reinforcement learning approach that addresses diversity collapse in GRPO-trained VLMs by incentivizing divergent thinking across multiple solutions, enabling deeper yet broader reasoning patterns.
This paper introduces VL-Calibration, a reinforcement learning framework that separates visual and reasoning confidence in large vision-language models to address confidently incorrect predictions. It estimates visual certainty from image-perturbation grounding and token entropy, then applies token-level advantage reweighting to improve calibration and visual reasoning accuracy.
This paper studies efficient reasoning in LLMs, using RL to incentivize short accurate trajectories, with findings on training stages, rewards, and generalization across models from 0.6B to 30B parameters.
This paper presents FIPO, a reinforcement learning algorithm that overcomes reasoning bottlenecks in LLMs by addressing coarse-grained credit assignment in GRPO-style training, where outcome-based rewards fail to distinguish critical logical pivots from trivial tokens.
This paper introduces Agentic Proposing, a framework that uses a specialized agent with Multi-Granularity Policy Optimization (MGPO) to dynamically select and compose modular reasoning skills for synthesizing high-precision training trajectories.
This paper studies policy entropy collapse as a bottleneck in RL for reasoning language models, showing an empirical relationship between entropy and downstream performance that makes the performance ceiling predictable. It derives entropy dynamics from the covariance between action probability and logit updates, then proposes Clip-Cov and KL-Cov to preserve exploration and improve downstream performance.
This paper introduces LatentSeek, a test-time instance-level adaptation framework that uses policy gradients and self-generated rewards to iteratively optimize an LLM's latent representations without updating model parameters. It improves reasoning across GSM8K, MATH-500, and AIME2024 while typically converging within only a few iterations.
This paper shows that pure reinforcement learning can directly elicit advanced reasoning behaviors in LLMs without human-labeled reasoning traces. The proposed framework induces self-reflection, verification, and adaptive strategy use, leading to strong gains on math, coding, and STEM reasoning tasks.
This paper introduces DeepSeekMath 7B, combining a carefully engineered web-scale math data selection pipeline with Group Relative Policy Optimization (GRPO), a PPO variant. The approach improves mathematical reasoning while reducing PPO's memory usage, reaching strong competition-level MATH performance without external tools or voting.
15 papers
SafeEvolve uses safety evidence from on-policy agent trajectories to co-evolve runtime safety prompts, hierarchical skills, and the policy. It combines bounded, auditable harness updates with harness-use SFT and harness-augmented RL using verifier-decomposed rewards, improving the balance between safety and task utility during multi-step interactions.
This paper proposes DART-SD, a topology-aware self-distillation framework that represents multi-turn tool execution as an Interaction-State Transition Graph (ISTG), preserving the diamond-shaped alternatives induced by order-independent sub-goals. It identifies the Critical Topological Breakpoint (CTB) in failed student rollouts and applies supervision only to retrieved recovery steps, protecting valid prefixes while progressively improving tool-use behavior.
ContextPilot equips long-horizon agents with planning, long-term memory, and soft context-offloading tools to keep working context compact. Its context-aware partial rollout and action-level credit assignment focus exploration and policy updates on consequential context-editing decisions, improving long-context QA and deep-search performance.
EnvHarness wraps static agent environments with composable plug-in components that reshape their behavior while preserving the original logic and verifiers. Its automated EnvRigger diagnoses policy weaknesses from trajectories and synthesizes targeted environments, providing stronger learning signals for reinforcement learning and continuous policy-environment co-evolution.
This paper introduces Skill Self-Play, a reinforcement learning framework in which a proposer, solver, and dynamic skill controller co-evolve through skill-conditioned task generation, frontier exploration, and feedback-driven skill-library updates. It combines reliable skill-specific verification with open-ended task diversity to improve tool use and reasoning across diverse LLM backbones.
OpenForgeRL is an open-source framework for end-to-end reinforcement learning of agents inside the stateful, multi-process inference harnesses and environments used at deployment. A lightweight recording proxy converts harness model calls into data for standard RL stacks, while a Kubernetes orchestrator isolates and scales rollouts across tool-use and multimodal GUI environments.
This paper proposes OPID, which extracts hierarchical episode- and step-level skills from completed on-policy trajectories and converts their effect into token-level self-distillation advantages alongside the outcome advantage. Its critical-first routing supplies dense, distribution-matched hindsight supervision, improving agent performance, sample efficiency, and robustness on embodied, web-shopping, and search-based tasks.
This paper proposes Group-Graph Policy Optimization (G2PO), which converts sampled interaction trajectories into a global state-transition graph to reduce variance in state-value estimation for long-horizon agentic reinforcement learning. Its group-aggregation values and edge-centric advantages provide fine-grained credit assignment under sparse, delayed rewards, improving success rates by up to 22.2% over GRPO.
Spreadsheet-RL is an RL fine-tuning framework for training specialized spreadsheet agents in a realistic Microsoft Excel environment, addressing complex multi-step workflows that prompting-based agents struggle with. It adds automated start-goal spreadsheet data collection, a multi-turn Spreadsheet Gym with sandboxed Excel tools, and a Domain-Spreadsheet benchmark to improve real-world spreadsheet automation.
This paper introduces Actor-Refiner collaboration to address the multi-scale credit assignment problem in search-integrated reasoning RL, where sparse trajectory-level rewards fail to distinguish high-quality reasoning from fortuitous guesses, reducing redundant or misleading search behaviors.
SkillRL distills raw agent trajectories into a hierarchical SkillBank, retrieves general and task-specific heuristics adaptively, and recursively evolves the skill library alongside the policy during reinforcement learning. This reduces token overhead while improving generalization and performance across embodied, web-shopping, and search-augmented tasks.
This paper introduces Arena-RL, a reinforcement learning framework that trains LLM-driven agents to play visual games via vision-language action models, focusing on policy improvement from interactive game feedback. It demonstrates that reward-driven optimization over game trajectories can significantly improve strategic decision-making and generalization across game environments.
This paper introduces ARPO, an agentic RL algorithm for training multi-turn LLM agents that must balance long-horizon reasoning with stepwise tool interactions. It uses entropy-adaptive rollout sampling and advantage attribution over tool-use steps, improving performance across computational reasoning, knowledge reasoning, and deep search benchmarks with roughly half the tool budget of prior trajectory-level RL methods.
This paper introduces Search-R1, an RL framework where LLMs learn to autonomously generate search queries during step-by-step reasoning with real-time retrieval, improving their ability to acquire external knowledge and up-to-date information.
This paper introduces Search-o1, a framework that enhances large reasoning models with an agentic retrieval-augmented generation mechanism and a Reason-in-Documents module for refining retrieved documents, addressing knowledge insufficiency in extended reasoning processes.
1 paper
This paper proposes SRPO, a reinforcement learning framework for vision-language-action models that replaces sparse binary rewards with progress-wise rewards derived from the model's own successful trajectories. It uses latent world-model representations to measure behavioral progress robustly and achieves state-of-the-art manipulation success on LIBERO with far fewer RL steps.
Paper
Code
Project
Hugging Face