Topic
Post-training turns a pretrained model into a useful system through supervised fine-tuning, preference optimization, reinforcement learning, distillation, and targeted data generation. The choice of method should follow the desired behavior, available feedback, and evaluation setup rather than a preference for one algorithm.
RLHF
RLAIF
Preference optimization
Distillation
Latest research
Zhejiang University
KnowGPO groups search-agent steps by accumulated task-relevant knowledge and rewards newly acquired facts. Controlled Qwen3-8B comparisons show gains over GRPO on four evaluation splits, but the method requires reference answers, successful training trajectories, and an external LLM verifier. Several narrative claims in the first version disagree with its tables.
Tsinghua University
ActionOPD distills black-box agent teachers by rewarding semantic agreement between sampled student and teacher actions. It needs teacher generation rather than token probabilities or aligned tokenizers. Experiments report cross-benchmark transfer, alongside mixed success-versus-reliability results against token-level OPD.
Zhejiang University
A common evaluation compares ten agentic self-distillation methods with GRPO across Qwen3.5-2B, 4B, and 9B on four agent benchmarks. Across 132 training runs, gains depend strongly on the setting, final scores can conceal unstable learning, and additional training time often fails to improve performance.
Fin.ai
The paper probes whether reference-conditioned self-distillation actually locates and corrects reasoning errors. Generating a critique before scoring gives more error-specific feedback in the tested models, and small Knights & Knaves training experiments improve with a critique-conditioned term. Every critique has access to a reference solution.
Tsinghua University
BranchOPD selects decision points using student uncertainty and teacher–student disagreement, samples student continuations from shared prefixes, and distills teacher feedback on the resulting branches. Protocol-boundary repair (PBR) separately addresses repetition and malformed response boundaries. Experiments cover mathematical reasoning and three agent benchmarks.
Xiaomi
Xiaomi’s MiMo-V2.6 report details large-scale mixed-task agentic RL: long-horizon batches, diverse environments and harnesses, and agentic grading beyond binary test rewards. It reports training-cost curves, router-stability experiments, infrastructure failures, and a subsequent multi-teacher distillation stage.
NVIDIA
NeMo-DCR transfers compressed weight changes between training and rollout clusters while reconstructing the same parameter and buffer bits as a dense refit. Cross-region benchmarks report large synchronization speedups, with separate small-scale training experiments testing recovery from interrupted updates.
University of Illinois Urbana-Champaign
SURGE recombines updates from two checkpoints of the same RL run, retaining a leading spectral component from an accurate anchor and adding complementary donor updates. Across two 1.5B math histories and one 7B coding history, the constructed policies improve selected accuracy measures while generating fewer tokens than their anchors.
National University of Singapore
RISED uses shared behavioral rubrics to select rollout groups across agent environments and to condition an on-policy self-distillation teacher. It improves mean validation pass rate on two small-model backbones, while adding judge inference, teacher forward passes, and rollout oversampling.
Shanghai Jiao Tong University
MOPD-Router selects and weights teacher supervision at each token rather than assigning one domain teacher to a whole prompt. ExpertAlign uses each teacher’s shift from a shared pre-RL base to identify useful corrections, improving a nine-benchmark aggregate in four tested settings.
Shuai Dong
On-policy distillation can prepare a student for later RL even when its immediate accuracy gain is small. Controlled downstream recipes show stronger reported final performance from OPD starting policies, and a distillation objective ranking that reverses after RL.
Kuaishou
KwaiMind combines diffusion preference optimization, task-specific online RL, and on-policy distillation for commercial image editing. Dedicated rewards cover predicted click-through rate, text rendering, and product consistency; the report separates offline reward-based gains from an online image-selection experiment.
Alibaba Group
CLOOPD reuses each teacher-scored batch for multiple student updates, separating the cost of acquiring supervision from the compute spent learning it. In one math-distillation setup, three actor passes nearly match a longer TOP-D run with fewer teacher tokens and allocated GPU-hours, while higher endpoint quality costs more compute.
Meta FAIR
WeightBridge maps trainer and rollout parameter layouts, then plans direct, balanced transfers of useful shards. On H100 deployments from 16B to 1T parameters, it sharply reduces GPU stalls during weight synchronization; the reported gains concern transfer overhead, not total RL training time.
Jie Zhao
Category-specific software-engineering experts alternate agentic RL with replay of their own successful trajectories, then transfer their gains into one student through label-routed on-policy distillation. The study tracks category regressions hidden by aggregate benchmark scores.
Juzheng Zhang
ActObs includes environment observations in the supervised loss on existing agent trajectories. The resulting initialization supports different exploration behavior during subsequent RL, with gains that depend on model size and evaluation sampling budget.
Yuxiao Yang
On-policy distillation can suppress a student’s stopping behavior when teacher and student favor different termination tokens. Grouping operationally equivalent EOS tokens at the probability level mitigates this mechanism, while a separate late-stage source of length inflation remains unresolved.
Together AI
Score centering removes an accumulating drift term caused by differences between rollout and training engines. Experiments test when this additive correction stabilizes quantized or stale-policy RL, and when it benefits from being combined with importance sampling.
Yan Yu
RetireOPD first trains a teacher with privileged task skills, then combines on-policy distillation with RL for a student that cannot access those skills. An adaptive rule retires the teacher once transfer stalls and the student reaches sufficient task competence.
AI Singapore
SEA-LION v4.8 adapts Nemotron models to Southeast Asian languages through continued pretraining, supervised fine-tuning, online on-policy distillation, and model merging. The report connects regional language performance to both data coverage and tokenizer constraints.
Dialpad Inc.
A controlled study compares SFT with LoRA, GRPO, and their sequential combination across six Qwen3 model sizes. SFT leads most in-distribution tool-calling settings, while RL’s cross-dataset advantage is smaller and dataset mixing remains consistently useful.
RadixArk
Miles v0.1 describes a full-stack post-training system spanning rollout generation, optimization, and weight synchronization. Its reference run demonstrates asynchronous terminal-agent RL with a 744B-parameter MoE model and makes the system’s supported configurations explicit.
Yang Li
RISE constructs a distillation teacher by extrapolating a model’s own RLVR training trajectory. Alternating reward-based updates with token-level distillation improves the tested reasoning and agent policies while reusing their sampled rollouts.
Shanghai Jiao Tong University
CUA-Universe turns desktop applications into shared-state GUI and CLI environments, then generates tasks and training trajectories. Supervised fine-tuning teaches a 9B model to coordinate the two interfaces with higher task scores and lower interaction costs.
Tsinghua University
An empirical study of how prompt difficulty and reasoning length affect on-policy distillation. Eight selected hard math problems approach the performance of a 17K-problem baseline, while repeated rollout generation and dense teacher supervision remain part of training.
Qwen Team, Alibaba Group
Terminal-Universe reconstructs reusable terminal environments from recorded agent trajectories, then generates new tasks within projects, across related codebases, and over multiple user rounds. SFT experiments suggest that expanding environment diversity can produce more useful supervision than adding queries or solutions to existing environments.
Sogang University
A GRPO replay method that reuses past rollout groups according to their remaining learning value and compatibility with the current policy. Experiments examine when replay can replace costly fresh environment interactions in reasoning and agent training.
New York University
An empirical study of how to combine on-policy distillation with RLVR. Training with distillation first and RL second produces strong logic-reasoning gains, while the advantage over the strongest alternatives is less decisive on math.
Anthropic
An Anthropic study and LessWrong discussion examining how reward hacking learned in deliberately vulnerable RL environments can transfer to harmful behavior in other evaluation scenarios. The findings also show important limits to that transfer.
Xingyu Shen
A method for preserving reasoning diversity during RLVR by conditioning the target model on partial trajectories produced by a smaller, weaker model.
Maciej Besta
A compute-centric systematization of RL-style reasoning-model post-training, covering PPO and GRPO as well as intra-model, inter-model, synchronous, and asynchronous parallelism.
Z.ai
An official GLM-5.3 release note describing a post-training-only update on the same base model as GLM-5.2, with expanded executable environments for complex coding, security, and long-horizon professional workflows.
Moonshot AI
A technical report on Kimi K3, a 2.8T-parameter sparse multimodal model with 104B active parameters and a one-million-token context window, post-trained with reinforcement learning across general, agentic, coding, and reasoning domains.
Steven Byrnes
A community argument that RLVR is operationally important but that most language-model knowledge and available reasoning strategies still originate in pretraining and supervised imitation.
Jialian Li
A technical report on Athena-Brain-8B, an on-device embodied model trained through general supervised fine-tuning, general reinforcement learning, embodied-expert training, and model merging.
Yufei Li
A production-oriented technical report on a generative reasoning re-ranker trained with semantic-ID mid-training, teacher-trace distillation, on-policy distillation, and reinforcement learning from verifiable ranking rewards.
Sakana AI
A technical report on Fugu and Fugu-Ultra, language-model orchestrators trained to construct query-adaptive scaffolds for teams of heterogeneous LLM agents.
Z.ai
An official release of the 750B-A40B GLM-5.2 model, combining a one-million-token context, IndexShare sparse-attention reuse, and larger-scale agentic reinforcement learning for long-horizon tasks.
Ang Li
A model-family report on Ling-2.6 and Ring-2.6, combining architectural migration, long-context efficiency work, token-efficient reasoning objectives, and asynchronous agent reinforcement learning at trillion-parameter scale.
MiniMax
An official release of MiniMax-M3, a natively multimodal sparse model with about 428B total and 23B active parameters, a one-million-token context window, and agent-oriented coding and cowork capabilities.
CarolusRenniusVitellius
A concise reading map of weight-level and behavioral evidence on whether RLVR mainly reweights existing capabilities or creates new reasoning mechanisms.
MiniMax
A technical report on the MiniMax-M2 family, pairing a 229.9B-parameter sparse MoE with agent-generated, verifiable trajectories and Forge, a scalable reinforcement-learning system for long-horizon agents.
Z.ai
An official GLM-5.1 model update focused on keeping an agent productive across longer coding and engineering runs through repeated execution, inspection, diagnosis, and strategy revision.
Cursor
A technical report on Composer 2, a specialized coding model trained through continued pretraining followed by large-scale reinforcement learning on long-horizon software-engineering tasks.
Z.ai
A GLM-5 technical report centered on agentic engineering, combining a more efficient long-context architecture with asynchronous reinforcement-learning infrastructure and agent RL for complex, long-horizon software tasks.
DeepSeek-AI
DeepSeek-R1 documents a reasoning-model training pipeline centered on reinforcement learning, including a zero-style RL stage and a staged route that combines supervised data with RL.
Kimi Team
A technical report on scaling multimodal language-model reinforcement learning with long-context training, policy optimization, and data-selection strategies.
DeepSeek-AI
DeepSeekMath combines a math-focused data pipeline with supervised fine-tuning and reinforcement learning, and introduces Group Relative Policy Optimization.
Meta FAIR
This work studies language models that generate candidate responses and also provide the preference signal used to improve subsequent iterations.
Google DeepMind
ReST alternates offline sample generation with policy improvement, allowing generated data to be reused rather than coupling every update to fresh online rollouts.
Stanford University
DPO reformulates preference optimization as a direct classification-style objective, avoiding an explicit reward-model training stage and online policy optimization.
Anthropic
Constitutional AI uses written principles and model-generated critiques and preferences to reduce direct dependence on human harmlessness labels during alignment training.
OpenAI
The InstructGPT work combines demonstrations, preference comparisons, reward modeling, and PPO to align language-model behavior with user instructions.
OpenAI
A large-scale study of training summarization models from human comparisons using reward modeling and reinforcement learning.