v0.3.3

Overview

This is a bug fix update that addresses many bugs present in versions 0.3. We recommend all users currently using versions 0.3.0/0.3.1/0.3.2 upgrade to this new version.

Explorer

1. Over Rollout: This mechanism allows the explorer to proceed with fewer tasks than the full batch size. It effectively increases throughput in scenarios where some tasks take significantly longer to complete than others.
2. Make the prompt truncation configurable
3. Fix logprobs calculation when temperature is not 1.0
4. Support rope scaling
5. Support loading custom chat template files
6. Support recording workflow running status
7. Optimize the aggregation of workflow metrics

Trainer

1. Update ppo policy loss calculation
2. Fix loss aggregation for kl loss and entropy loss
3. Optimize Trainer checkpoints saving

Buffer

1. Support registering custom Task Reader
2. Support token-level reward
3. Fix some bugs

Others

1. Add "Learn to Ask" and "Frozen Lake" examples
2. Update Dockerfile

What's Changed

• Fix doc/config issues about prompt/response/sequence lengths by @yanxi-chen in #370
• Limit the number of historical document versions by @pan-x-c in #372
• Add learn_to_ask example by @chenyushuo in #356
• Update main readme by @yanxi-chen in #374
• Fix default_sampling_params and simple_workflow by @chenyushuo in #373
• Fix metrics in trainer by @chenyushuo in #381
• Minor updates for BOTS example by @HYLcool in #385
• Fix vLLM prompt logprobs calculation by @pan-x-c in #384
• Fix alfworld dataset loading to use correct train/test split by @shiweijiezero in #378
• Bug fix when set total_steps. by @chenyushuo in #386
• Add more useful options in OptimizerConfig by @garyzhang99 in #371
• Add rope_scaling and rope_theta to config by @chenyushuo in #390
• Add chat_template_path and trust_remote_code by @hiyuchang in #379
• Add loss_agg_mode for kl and entropy_loss by @pan-x-c in #388
• Bug fix in benchmark ckpt loading and megatron hf save by @chenyushuo in #392
• Support Over Rollout by @pan-x-c in #376
• Make Alfworld Rollout Parallel by @hiyuchang in #393
• Make buffer reader registerable by @pan-x-c in #395
• [Example] Frozen_Lake by @hiyuchang in #375
• Add token_level_reward to Experience by @chenyushuo in #404
• Support recording workflow running status by @pan-x-c in #397
• Bug fix for trainer_state saving by @chenyushuo in #408
• Fix dynamic timeout by @pan-x-c in #409
• Typo fix by @chenyushuo in #411
• Fix repeat times in evaluation by @chenyushuo in #410
• Add truncate_status to experience by @hiyuchang in #407
• Add trainer_strategy and save_hf_checkpoint by @pan-x-c in #412
• Save bf16 model by @chenyushuo in #414
• Add Docker image build and push workflow by @pan-x-c in #413

Full Changelog: v0.3.2...v0.3.3

v0.3.2

Overview

This is a bug fix update that addresses many bugs present in versions 0.3. We recommend all users currently using versions 0.3.0 and 0.3.1 upgrade to this new version.

Buffer

1. Support task scheduler and selector in task dataset
2. Add BOTS: Online RL task selection for efficient LLM fine-tuning (paper).
3. Extract the PriorityFunction of the replay buffer as a customizable module.

Explorer

1. Update vLLM to v0.11.0
2. Fix logprobs, top_k, top_p, temperature mismatch when using vLLM's OpenAI API server
3. Fix torch cache conflicts when enforce_eager is False
4. Simplify Workflow Inferface

Other Modules

1. Optimize monitor metrics organization
2. Optimize and simplify Config and config manager
3. Add more algorithm and examples

What's Changed

• Simplify trainer config by @hiyuchang in #329
• Simplify Workflow Interface by @pan-x-c in #330
• Update off-policy RFT documentation by @yanxi-chen in #335
• Simplify Buffer and Explorer Config by @pan-x-c in #333
• Update rec example by @yanxi-chen in #337
• Sync the config manager with the latest codebase by @hiyuchang in #332
• Add taskset scheduler by @chenyushuo in #326
• Split Storage Config by @pan-x-c in #338
• Fix some metrics by @hiyuchang in #339
• Refactor Priority Function by @chenyushuo in #344
• Bug fix in unittest by @chenyushuo in #346
• [Example] Mix algorithm with VLM by @hiyuchang in #342
• Add example for experience replay by @yanxi-chen in #345
• Fix Bug: group_rewards Un-assigned value by @shiweijiezero in #343
• Fix openai client logprobs calculation by @pan-x-c in #347
• Fix openai API server setup by @pan-x-c in #348
• Update readme by @yanxi-chen in #349
• Update vLLM to 0.11 by @pan-x-c in #350
• bug fix in plugin_loader by @chenyushuo in #354
• Fix/email search by @chenyushuo in #351
• Enable System Metrics Recording in MLFLOW by @hiyuchang in #362
• Add generation args to ModelConfig by @chenyushuo in #357
• Add std_thresholdoption to StepWiseGRPOAdvantageFn, to filter out zero-grad group samples. by @garyzhang99 in #363
• Fix microbatch loss scale when loss_agg_mode is "token-mean" by @yanxi-chen in #336
• Add weight_decay in OptimizerConfig by @hiyuchang in #364
• Fix taskset and eval_tasksets path by @hiyuchang in #367
• Fix torch compile cache dir conflicts when enforce_eager=False by @pan-x-c in #368
• Examples for BOTS by @ShenQianli in #353
• Release Trinity-RFT 0.3.2 by @pan-x-c in #369

New Contributors

• @ShenQianli made their first contribution in #353

Full Changelog: v0.3.1...v0.3.2

v0.3.1

Overview

Agentic RL

1. Add more agentic RL examples using agent frameworks (e.g. AgentScope)
2. Provide Debug mode for workflow developers
3. Add examples for RL in non-verifiable domain: trainable RULER reward, rubric-as-reward

Framework Enhancement

1. Support multi-stage training
2. Support using environment variables in configuration file
3. Support LoRA
4. Enhance checkpoint saving process
5. Enhance experience replay mechanism for priority queue buffer
6. Add algorithms: group-relative REINFORCE variants
7. Update vLLM to 0.10.2

Documentation

1. Add Chinese Docs
2. Rewrite Developer Guide

What's Changed

• Support new task ordering methods by @HYLcool in #265
• Fix problem in get_openr1_dataset.py in chord example by @garyzhang99 in #266
• Replace /PATH/TO to ${oc.env:TRINITY_XXX} by @chenyushuo in #267
• Support multi-stage training by @pan-x-c in #268
• [Example] Policy model as its own reward model by @hiyuchang in #270
• Dev/update agentscope react example version by @garyzhang99 in #275
• Normalize Trainer by @pan-x-c in #271
• Refactor workflow to async workflow by @chenyushuo in #276
• Support Chinese Docs by @hiyuchang in #277
• Support vLLM 0.10.2 by @pan-x-c in #278
• AgentScopeV1.0 WebSearch Workflow(simple react + search api) by @garyzhang99 in #264
• Add enable_activation_offload configuration option by @nkkarpov in #281
• Update data-juicer version in toml by @chenyushuo in #286
• Improvement in config by @chenyushuo in #288
• Add loss-agg-mode for policy loss by @hiyuchang in #294
• Explorer provides OpenAI API compatible inference service by @pan-x-c in #289
• Fix absmethod in workflow by @chenyushuo in #297
• Add LoRA mode by @hiyuchang in #291
• Enhance support for multi-modal models by @pan-x-c in #298
• Group-relative REINFORCE Families by @yaochaorui in #292
• Optimize Installation and Development Doc by @pan-x-c in #301
• Update Readme by @pan-x-c in #302
• Refactor checkpoint save by @chenyushuo in #299
• Refactor AgentScope ReAct Agent workflow example by @pan-x-c in #303
• Update Workflow Developement Tutorial by @pan-x-c in #310
• Update chord with tooluse example by @garyzhang99 in #313
• Debug Mode for workflow developers by @pan-x-c in #314
• [BUGFIX]Fix tokenizer bug when getting action masks with enable_thinking arguments by @garyzhang99 in #316
• Add task_count by @hiyuchang in #307
• Update FAQ by @hiyuchang in #320
• Refactoring EmailSearchWorkflow and EmailSearchAgent to adapt to the latest version of Agentscope. by @chenyushuo in #321
• Non-verifiable Medicine QA Task by @hiyuchang in #317
• Add batch level std calculation by @garyzhang99 in #311
• Implement serial saving by @chenyushuo in #322
• Enhance experience replay for priority queue buffer by @yanxi-chen in #306
• Fix example config typo by @garyzhang99 in #323
• Simplify Config by @pan-x-c in #325
• Update config from ray cluster by @hiyuchang in #324
• Support AgentScope Workflow Function by @pan-x-c in #327

Full Changelog: v0.3.0...v0.3.1

v0.3.0

Overview

Framework Development

Buffer Module

1. Use Operator Interface to replace the original AddStrategy. Operator can perform various transformations on experience data in a pipeline manner. [Breaking Change]
2. Add TaskPipeline and ExperiencePipeline for task and experience data preprocessing.
3. Support calling Data-Juicer services in both TaskPipeline and ExperiencePipeline, and resolve some dependency conflicts.
4. Refactor SQL/FILE storage. SQL can store SFT/DPO/Rollout/Experience data. SQL and FILE support parsing multi-turn SFT data with tools. [Breaking Change]

Trainer Module

1. Support FSDP2 backend
2. Support Megatron backend
3. Support Qwen2.5 VL muti-modal models [Experimental]

Explorer Module

1. Support Qwen2.5 VL multi-modal models [Experimental]
2. Workflow supports running in async mode.
3. ModelWrapper provides openai.AsyncOpenAI interface.

Utils Module

1. Enhance logger and support printing logs of different actors to different files under the checkpoint dir
2. Enhance wandb and mlflow monitor

New Algorithms

1. AsymRE
2. sPPO
3. RULER
4. TOPR and CISPO

New Workflows

1. General Multi-turn Email Search

Others

1. Support uv
2. Refactor README and documents
3. Fix many bugs

What's Changed

• Support FSDP2 by @chenyushuo in #204
• Update veRL to 0.5.0 by @pan-x-c in #195
• Enhance Buffer Data Processing Pipeline by @pan-x-c in #175
• Remove AddStrategy by @pan-x-c in #211
• Add sft tools example and tests by @garyzhang99 in #210
• Cleaning up the old-version data processor by @HYLcool in #213
• Update main readme for v0.2.1 by @yanxi-chen in #216
• Add data_process figure by @hiyuchang in #218
• Enhance Logger by @pan-x-c in #217
• Bug fix for experience display in wandb by @chenyushuo in #224
• Fix links in readme by @yanxi-chen in #225
• Fix alfworld prompt typo by @garyzhang99 in #222
• Enhance SFT/DPO reader by @pan-x-c in #226
• AsymRE by @yaochaorui in #187
• [example] An email search workflow by @hiyuchang in #230
• Bug fix for FSDP offload by @chenyushuo in #233
• Refactor Storage by @pan-x-c in #227
• sPPO by @yaochaorui in #232
• Specify attempt in error message in run_with_retry by @vadimkantorov in #241
• Add issue template by @pan-x-c in #246
• [Example] GRPO on GSM8K with RULER reward by @hiyuchang in #239
• Add TOPR and CISPO algorithm by @garyzhang99 in #185
• Support Multi turn SFT with tools by @pan-x-c in #245
• Support Megatron by @chenyushuo in #219
• Support Multi-Modal LLM by @hiyuchang in #234
• Add log_table function for mlflow by @hiyuchang in #249
• Update the example of human in the loop by @HYLcool in #247
• Keep SQL Experience Buffer behavior consistent with previous versions by @pan-x-c in #248
• Update MoE training in example by @chenyushuo in #251
• SFT tools schema fix by @garyzhang99 in #252
• Merge verl-related config into default config by @hiyuchang in #256
• Update config manager for 0.3.0 by @chenyushuo in #257

Full Changelog: v0.2.1...v0.3.0

v0.2.1

Overview

1. Agentic RL
   1.1 The rollout model can now be accessed directly via the OpenAI API, reducing migration costs.
   1.2 Supports general multi-step workflows without requiring concatenated experience data.
   1.3 Introduced AddStrategy to facilitate group-based advantage/return calculations (experimental; will be integrated into the buffer module in future versions).
   1.4 Added a ReAct Agent RL example based on the AgentScope framework.
   1.5 Enhanced the Alfworld example into a general multi-step workflow.

2. Async / Offline RL
   2.1 Refactored RunnerPool to Scheduler, enabling asynchronous scheduling and management of multiple workflow runners.
   2.2 Added a priority queue buffer to reduce idling caused by speed differences between Explorer and Trainer through experience sorting and reuse.
   2.3 Introduced Synchronizer to manage model weight synchronization between Explorer and Trainer, supporting dynamic synchronization.
   2.4 Added tutorials on using the Synchronizer.

3. Add a benchmark tool for quick verification.

4. Added support for more RL algorithms (e.g., CHORD， DAPO, GSPO, RAFT).

5. Updated vllm to 0.10.0 and verl to 0.4.1.

6. Fixed numerous bugs.

What's Changed

• Add a switch for progress bar in _HFBatchReader by @yanxi-chen in #126
• Add dapo reward by @hiyuchang in #114
• Add readme_zh by @hiyuchang in #127
• Fix a typo in readme by @hiyuchang in #128
• ModelWrapper automatically record Experience by @pan-x-c in #123
• Add continue_from_checkpoint by @hiyuchang in #129
• Merge verl v0.4.1 by @hiyuchang in #125
• Fix vllm nccl sync error by @pan-x-c in #132
• Add more unittest command by @pan-x-c in #133
• Add Step-wise Workflow by @pan-x-c in #130
• Add workflow and example for toolcall training using ToolAce dataset by @garyzhang99 in #134
• Rename data scripts for examples and refine toolcall example readme by @garyzhang99 in #137
• Add sft example by @hiyuchang in #138
• Fix buffer.total_epochs not working in SFT/DPO by @pan-x-c in #140
• Fix priority queue implementation and enhance testing by @pan-x-c in #135
• Update some details in tutorial by @hiyuchang in #144
• [exmaples] Updated the OPMD config. by @yaochaorui in #145
• Rollout openAI API compatible with vllm 0.8.5 by @pan-x-c in #146
• Standardize Experience and Sample Strategy by @pan-x-c in #141
• Add fused_kernel_options by @chenyushuo in #150
• Fix MATH readme by @hiyuchang in #151
• Calculate advantage in Explorer by @pan-x-c in #148
• Add Synchronizer by @chenyushuo in #131
• Add run_id for single-turn workflows by @hiyuchang in #152
• Bug fix for Scheduler and torch.tensor by @chenyushuo in #156
• Add Step-wise GRPO Advantage by @pan-x-c in #153
• Fix a bug in args_pass by @hiyuchang in #155
• Add decoupled evaluation workflow by @lingzhq in #142
• Add some training tricks for RLVR by @hiyuchang in #147
• GSPO-token policy loss function by @nkkarpov in #154
• Add tool call usage from our vllm model by @garyzhang99 in #161
• Refactor Trainer.train to async function by @chenyushuo in #164
• Distinguish repeatable/non-repeatable workflows by @hiyuchang in #162
• Add auto release for synchronizer by @chenyushuo in #166
• Fix multi-turn logprobs by @pan-x-c in #170
• Bug fix in Synchronizer by @chenyushuo in #171
• Update vLLM to 0.10.0 and add max_model_len by @hiyuchang in #172
• Add agentscope react multi-turn toolcalls example by @garyzhang99 in #165
• Add step-wise workflow test by @hiyuchang in #173
• Add MLFlow monitor by @pan-x-c in #179
• [Feat] Allow user to set train_batch_size by @hiyuchang in #177
• [example] Alfworld with General Multi-Step Workflow by @hiyuchang in #169
• feat: add RAFT alfworld example with reflection support by @shiweijiezero in #174
• Add general multi-step figure by @hiyuchang in #186
• Add benchmark by @chenyushuo in #178
• Fix custom_fields in experiences by @pan-x-c in #191
• Add Document for Synchronizer by @chenyushuo in #190
• Bug fix in load_plugins and explorer by @chenyushuo in #193
• Add CHORD algorithm example by @garyzhang99 in #194
• Fix problem in math_mix config by @garyzhang99 in #196
• Fix plugin_loader by @pan-x-c in #201
• Add unittest for mix by @hiyuchang in #200
• Set truncate_prompt_tokens in SamplingParams, silently truncating very large prompts and preventing vllm from throwing exception by @vadimkantorov in #198
• Support multi-version docs by @pan-x-c in #203
• Fix/fix agentscope tools example docs by @garyzhang99 in #205
• Auto-set pad_token_id when the default is None and not set in the buffer config. by @yaochaorui in #188
• Fix agentscope react example readme by @garyzhang99 in #206
• Add max_prompt_tokens by @chenyushuo in #202
• Release v0.2.1 by @pan-x-c in #208

New Contributors

• @yaochaorui made their first contribution in #145
• @nkkarpov made their first contribution in #154
• @vadimkantorov made their first contribution in #198

Full Changelog: v0.2.0...v0.2.1

v0.2.0

Overview

1. Refactor Algorithm-related modules, see #59 for details
2. Propose an SFT/GRPO-mixed algorithm
3. Unify Sync/Async RL via sync_interval, and support one-step async pipeline
4. Refactor the data processor module, and support processing input tasksets and experience data
5. Refactor RunnerPool to Scheduler to support automatic fault tolerance and fine-grained scheduling
6. Refactor Explorer to a fully asynchronous implementation
7. Support running multiple Explorer instances simultaneously
8. Update vLLM to v0.9.1, verl to 0.4.0
9. Support reward functions in RM-Gallery
10. Fix various bugs
11. Update the technical report (arXiv v2) with new features, examples, and experiments

What's Changed

• Add Policy Loss Functions by @pan-x-c in #62
• Refactor advantage computation, and delete RayPPOTrainer.fit by @yanxi-chen in #61
• Add unittest && bug fix by @chenyushuo in #65
• Add KL/Entorpy Fn by @pan-x-c in #64
• Refactor advantage computation (cont.) by @yanxi-chen in #68
• Refactor train step by @chenyushuo in #69
• Fix BasicEntropyLossFn by @shiweijiezero in #77
• Fix Conflicts with main by @pan-x-c in #75
• Add Sample Strategy by @pan-x-c in #78
• Add doc for SFT by @hiyuchang in #81
• merge verl 0.4.0 by @chenyushuo in #79
• [Feature] Add MIX algorithm by @hiyuchang in #83
• Refactor on select_keys by @chenyushuo in #84
• Add guideline for adding new algorithm by @pan-x-c in #85
• Update config manager by @chenyushuo in #86
• Update docs by @hiyuchang in #89
• Refactor state_dict_meta init by @chenyushuo in #90
• Unify async/sync RL by @pan-x-c in #91
• Support one-step ahead async RL by @pan-x-c in #93
• Refactor data module and support task pipeline in data processor by @HYLcool in #92
• Bug Fix in namespace by @chenyushuo in #96
• Unify ray actor creation method by @pan-x-c in #97
• Add ray timeline for profiling by @pan-x-c in #98
• Bug fix in explorer.eval by @chenyushuo in #99
• Async RL support multiple explorers by @pan-x-c in #100
• Refactor explorer loop by @chenyushuo in #101
• Record model version during model weight sync by @pan-x-c in #102
• Fix bugs in multi-node environments by @pan-x-c in #103
• Fix Incomplete Last Batch by @pan-x-c in #104
• Support Experience Pipeline by @HYLcool in #105
• Bug Fix && Add more logger info by @chenyushuo in #106
• Bug fix in alfworld by @chenyushuo in #107
• Add timeout to buffer reader by @pan-x-c in #108
• Add FAQ in docs by @hiyuchang in #109
• Optimize model weight sync process group by @pan-x-c in #112
• Add total steps to StorageConfig by @pan-x-c in #111
• Remove useless configs by @HYLcool in #113
• Add new scheduler with step granularity by @pan-x-c in #110
• Split group of tasks to multiple runners by @pan-x-c in #116
• Add AsyncPriorityQueue. by @chenyushuo in #115
• Refactor RewardFn by @hiyuchang in #118
• Add unique_id for each experience by @hiyuchang in #120
• Add a script to plot multi-run experiment results by @lingzhq in #122
• Update main readme with arxiv v2 by @yanxi-chen in #121
• Release Trinity-RFT v0.2.0 by @pan-x-c in #124

New Contributors

• @lingzhq made their first contribution in #122

Full Changelog: v0.1.1...v0.2.0

v0.1.1

Overview

1. Supports deployment of non-trained auxiliary models in the cluster, which can be used to provide rewards or other feedback in workflows.
2. Support more custom components (e.g., monitors), and support automatic loading of custom components
3. Support the use of file and database buffers in multi-node environments
4. Bug fix

What's Changed

• link to Trinity-Studio codes by @yxdyc in #54
• Update developer guide by @pan-x-c in #53
• Bug fix in auxiliary_models. by @chenyushuo in #55
• Decompose config_manager. by @chenyushuo in #57
• Fix constants by @hiyuchang in #63
• Support custom monitor by @pan-x-c in #66
• Add model_path to auxiliary_models. by @chenyushuo in #67
• Fix DLC mode by @pan-x-c in #71
• Wrap database in ray actor by @pan-x-c in #70
• Add workflow_args for fine-grained control by @pan-x-c in #73
• Support loading user-written plugin modules automatically by @pan-x-c in #74
• Refactor Buffer Reader by @pan-x-c in #80
• Wrap file writer in ray by @pan-x-c in #82
• Progress Bar by @shiweijiezero in #87
• Customized math workflows by @garyzhang99 in #88
• Bumping version to v0.1.1 by @pan-x-c in #94

New Contributors

• @yxdyc made their first contribution in #54
• @shiweijiezero made their first contribution in #87

Full Changelog: v0.1.0...v0.1.1

v0.1.0

Overview

Trinity-RFT is a general-purpose and unified framework for reinforcement fine-tuning of large language models.

We release Trinity-RFT v0.1.0 with our technique report

What's Changed

• Add GitHub Actions by @pan-x-c in #1
• fix some bugs for eval by @hiyuchang in #2
• Try to activate data module in the launcher by @HYLcool in #3
• Install from Dockerfile by @pan-x-c in #7
• refactor MultiTurnWorkflow class and add SciWorld ENV by @garyzhang99 in #9
• UPD on config_manager.py and docs by @chenyushuo in #4
• Add sft warmup before dpo by @hiyuchang in #12
• Update main readme by @yanxi-chen in #8
• Add examples directory by @hiyuchang in #14
• Add unittest on self-host runner by @pan-x-c in #15
• A trick to update wandb after each step by @xieyxclack in #16
• Add training service by @HYLcool in #13
• Add tensorboard monitor by @pan-x-c in #21
• Simplify wandb log and update default trainer config by @pan-x-c in #22
• fix: init_process_group failed when using ipv6 master address by @0x404 in #24
• fix: some hf dataset may need specified config name by @0x404 in #25
• Refactor on config_manager.py by @chenyushuo in #23
• Make 'get_exp_strategy' effective in sql by @hiyuchang in #26
• Add more unittest and support Qwen3 by @pan-x-c in #29
• Config Refactor by @chenyushuo in #27
• Add example_async_mode by @hiyuchang in #28
• Rollout use vLLM V1 engine by @pan-x-c in #31
• Fix in Config by @chenyushuo in #30
• Add CLI for Trinity Studio by @pan-x-c in #32
• Add bench mode by @hiyuchang in #35
• Fix n does not take effect in vLLM v1 engine by @pan-x-c in #36
• Add a switch for Qwen3 think mode by @pan-x-c in #37
• Refactor on TaskSet and Buffer by @chenyushuo in #34
• Add utils for PAI DLC by @pan-x-c in #38
• Add benchmark mode by @hiyuchang in #39
• Refactor workflow by @chenyushuo in #40
• Move algorithm_type from trainer to global_config by @chenyushuo in #42
• Async model support OpenAI compatible API by @pan-x-c in #41
• Add resettable workflow by @chenyushuo in #43
• Reorganize config by @pan-x-c in #46
• Update grpo and dpo examples by @hiyuchang in #48
• Update README.md and main.md by @yanxi-chen in #47
• Fix config manager by @chenyushuo in #49
• Update trinity config guide by @pan-x-c in #50
• Update README.md and main.md by @yanxi-chen in #52

New Contributors

• @pan-x-c made their first contribution in #1
• @hiyuchang made their first contribution in #2
• @HYLcool made their first contribution in #3
• @garyzhang99 made their first contribution in #9
• @chenyushuo made their first contribution in #4
• @yanxi-chen made their first contribution in #8
• @xieyxclack made their first contribution in #16
• @0x404 made their first contribution in #24

Full Changelog: https://github.com/modelscope/Trinity-RFT/commits/v0.1.0