* update rdagent cmd
* fix log error message
* use multiProcessing.Process instead of subprocess.Popen
* add traces to gitignore
* add user interactor in RDLoop (finance scenarios)
* add interactor (feedback, hypothesis) for quant scens
* fix the test_end in qlib conf
* add features init config, general instruction to qlib scenarios
* set base features for based exp
* fix bug when combine factors
* move traces folder to git_ignore_folder
* fix bug in features init
* fix quant interact bug
* fix logger warning error
* bug fixes
* modify rdagent logger, now it can set file output
* adjust cli functions and fix logger bug
* fix server port transport problem
* update server_ui in cli
* add web code
* fix CI problem
* black fix
* update web ui README
* update README
* update readme
* feat: rdkit for chemcotbench
* update qwen2.5&llama3.1 context
* fix: force failure on validation error and remove try/except in validator
* feat: unified error sample extraction (with test scripts)
* feat: set conda cache with .env
* feat: skip data eval if data pass in last evo
* fix: rm redundant param
* fix ui bug
* refactor: centralize assign_code_list_to_evo in MultiProcessEvolvingStrategy
* feat: add test_params.yaml generation and workspace cleanup improvements for finetune
* refactor: replace get_clear_ws_cmd with clear_workspace and update prompts for hard check criteria
* add bioprobench dataset
* fix: handle commas in training config extraction and refactor prompt includes
* bioprobench description
* add bioprobench readme
* feat: merge lora adapter for blackwell gpu
* feat: support for multi benchmarks in one job
* change dfficult aware content for training
* update difficulty-aware and logging principles
* fix: resolve variable name conflict in FTRunnerEvaluator
* set job id accuracy to minute
* feat(ui): display one selected metric per benchmark
* feat: store sota exp, and fix ws_ckp bug
* fix: truncate data.json in feedback
* fix: opencompass data for conda env
* fix: save only the last model
* feat: set log path and ws path
* fix: set overwrite_cache to avoid lock contention(through injecting params)
* feat: redirect stdout to file in localenv
* add pickle cache to dataset desc
* fix CI
* fix: remove redundant wrapper
* feat: set python_unbuffered
* move redirect stdout to env run
* fix a small bug
* move model folder
* feat(ui): display benchmark baseline
* fix: enrich scenario and benchmark description
* fix: rewrite runner eval to accept easier
* feat: compare with baseline when no SOTA
* update tablebench readme
* fix: switch back to single benchmark (for baseline)
* feat(ui): add ws path in ui
* refactor: update SOTA tracking to use DAG traversal and parent selection
* fix: prioritize local_selection in trace and refactor sibling retrieval logic
* refactor: unify error handling in feedback generation and update workspace injection
* feat: add skip_loop_error_stepname to control error skip step in LoopBase
* fix: set local_selection to NEW_ROOT for experiments without parent
* feat: set different ports for jobs
* feat: set different ports for jobs
* feat: add upper data size limit for LLM fine-tuning and update related prompts
* fix: replace get_truncated_stdout() with stdout for consistent output handling
* refactor: remove data.json from cache and workspace logic, focus on script-based reuse
* fix: rm target_scenario
* feat: add selective cache extraction and custom cache key for data processing
* fix(ui): bug when displaying tablebench
* fix: filter config in dataset_info.json
* feat: add test set, set valid set
* feat(ui): update test score, and set color for final decision
* feat: add test score for baseline and update ui
* fix: use [-100:] as test range
* feat: update data_stats in runner
* feat: wait for opencompass init when run multi jobs
* fix: adjust test&valid split
* feat: force to generate COT(with <think> token), and add answer format in scenarios.json
* feat: improve ui
* fix: unify benchmark volume mounts and set extra_volumes for conda env
* fix(ui): number color
* fix: update GPU memory handling to use total memory in GB and streamline code
* fix: set use_cot_postprocessor
* feat: add env_dict to config classes and merge env vars in Env run
* fix: let coder obey proposal
* fix(ui): direction bug and update chemcot core metirc
* fix: set consistent benchmark mount points and env vars for docker and conda
* fix: addintional target for LoRA
* feat: workspace dir log for benchmark running
* fix: tableInstruct path bug and update benchmark description
* feat: timeout for whole job
* fix: align FinanceIQ import to opencompass
* feat: use llm_judge for FinanceIQ
* feat: switch to turn on <think> or not
* feat: using scripts to redirect stdout, and run in different windows
* feat: sync litellm log
* fix: gpu memory format
* fix: escape special characters in benchmark desc
* fix: set data processing timeout to 1h
* feat: set valid_loss and save_best_model
* fix: inject timeout and stage
* fix: loss history extract logic
* feat: inject output dir
* feat: inject eval batch size
* feat: inject save_total_limit
* feat: update data prompt
* fix: escape shell special characters
* fix: tablebench visualization UI
* fix: move implementation validation to coder, and ignore injected params
* docs: add README for RL-PostTraining evaluation system
* Add AutoRL-Bench evaluation framework for RL post-training
* Add architecture documentation
* docs: update architecture and interface documentation for AutoRL-Bench
* improve doc
* fix
* refactor: YAML配置驱动
* feat: add RL Docker env, workspace test, and update project structure
* feat: 重命名 autorl_bench, 新增 RLWorkspace, 配置 Docker extra_volumes
* Add eval-only AutoRL-Bench pipeline
* sturcture clean
* docs: add autorl_bench README
* feat(rl): Implement RL post-training agent scaffold and example
* refactor: simplify RL scenario classes and update RL CoSTEER integration
* feat(rl): 调通 scaffold，mock 数据跑完 5 步循环
* feat(rl): 接入 LLM 生成代码，支持 model_path 传递
* feat(rl): Docker 执行框架，RLWorkspace.run() + RLPostTrainingRunner
* feat(rl): LLM 生成假设/反馈，完整 loop 跑通
* feat: add RL post-training entry point with configurable options
* refactor: simplify RL proposal and trace classes, update config and docs
* Update rl eval autorl_bench layout
* Update RL workflow and evaluation setup
* Integrate AutoRL-Bench evaluation in RL workflow
* feat(rl): 添加 --base-model/--benchmark CLI 参数，简化 RLTask
* feat(rl): Docker 环境动态选择 + example_agent 完整训练评测流程（无llm）
* fix(rl): 修复 feedback 传递 + 添加 verl 依赖
* refactor: remove unused validate in BenchmarkAdapter and add core utils module
* feat(rl): UI
* Refactor autorl_bench layout and docker entrypoint
* autorl_bench: add aider autoloop tool
* feat(rl): environment docker
* refactor: simplify aider autoloop tooling
* chore: update misc files
* feat(rl): yaml-driven dataset download & auto-download on startup
* feat(rl): yaml-driven dataset download & auto-download on startup
* Refactor RL eval runner and clean up
* Simplify RL eval runner and env
* rl: include litellm in RL docker image
* feat(rl): unified resource path & model repo_id structure
* feat(rl): refactor eval with OpenCompass & add training code template
* feat(rl): refactor eval with OpenCompass & add training code template
* feat(rl): delete test bench
* docs: add benchmark interface notes and TODOs for unified evaluation
* feat(rl): unified benchmark eval interface + shared configs
* feat(rl): 优雅
* feat(rl): prompt prososal+coder improve
* feat(rl): fix eval
* fix(rl): docker
* fix(rl): eval
* v 1.0 tmep
* benchmark v1.0
* benchmark v1.1
* benchmark v1.1: grading日志+代码去重
* benchmark v1.1: grading日志+代码去重
* benchmark v1.1: grading日志+代码去重+task description
* benchmark v1.2: fix
* benchmark v1.3: fix，example-agent ok，rdagent test，openhands develop
* benchmark v1.4: fix，example-agent ok，rdagent ok，openhands develop
* benchmark : add alfworld
* benchmark : update readme
* benchmark : update readme
* benchmark :
* chore: add eval bypass block and mark TODO in grading server
* benchmark
* benchmark
* benchmark
* benchmark
* alfworld
* alfworld
* benchmark
* rdagent
* rdagent
* benchmark
* benchmark：ui
* benchmark：delete docker + log
* 1
* alfworld
* ui
* alfworld
* readme
* alfworld
* parallex
* alfworld
* run
* eval gpu
* alfworld
* alfworld
* fix conda init in start.sh for non-interactive shells
Fallback to common miniconda paths when conda is not in PATH.
Fixes B200 pod startup failure (conda: command not found).
Made-with: Cursor
* simplify start.sh: read TRAINING_PYTHON from .env
No more conda detection logic. Just set TRAINING_PYTHON in .env.
Fallback to conda only if not set.
Made-with: Cursor
* use OPENHANDS_PYTHON from .env to run agent
start.sh now uses OPENHANDS_PYTHON for main.py execution,
since the parent process may be in a different conda env.
Made-with: Cursor
* feat: register OpenCode agent into autorl_bench framework
- Add agents/opencode/ with config.yaml, start.sh, README.md
- Include opencode-rl pipeline code (pipeline/, runner_fsm/, benchmarks/)
- Merge opencode-rl dependencies into autorl_bench requirements.txt
- Remove separate venv requirement, share main environment
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Update opencode agent, benchmarks, and eval configs
- Sync opencode-rl runner_fsm with latest simplifications
- Add smith benchmarks integration
- Update opencompass configs and server with GPU support + error handling
* Update OpenCode agent docs for external opencode-rl integration
- Document external repo architecture (opencode-rl as independent plugin)
- Add setup instructions for cloning and configuring opencode-rl
- Add architecture diagram showing RD-Agent ↔ opencode-rl interaction
- Document OPENCODE_RL_ROOT for custom paths
* feat: add smith benchmark discovery and per-sample evaluator
- Add smith/ module for dynamic benchmark discovery from rl-smith
- Add PerSampleEvaluator for per-sample scoring via vLLM
- Update utils.py to support script-based data download for smith benchmarks
- Update opencode agent config
* enforce RL-only in instructions.md; remove embedded opencode-rl
- instructions.md: prohibit SFT, require RL (GRPO/PPO) for all benchmarks
- remove agents/opencode/opencode-rl/ (runtime uses external OPENCODE_RL_ROOT)
Made-with: Cursor
* comment out OpenCode-only deps in requirements.txt
openai, httpx, python-dotenv, tenacity are for OpenCode agent's
separate environment. Keep peft and pydantic as shared deps.
Made-with: Cursor
* refactor: extract _kill_process_group, narrow exception catches
- run.py: replace 2x nested 3-level try/except with shared
_kill_process_group() using loop + specific exceptions
- server.py: except Exception → except (RuntimeError, ValueError, OSError)
- utils.py: except Exception → except requests.ConnectionError
Made-with: Cursor
* move kill_process_group to core/utils for reuse
Extract from run.py into core/utils.py so other runners
can also use it. Exported via core/__init__.py.
Made-with: Cursor
* add comments to run.py for workspace isolation and signal handling
Made-with: Cursor
* remove OpenCode-only deps from requirements.txt entirely
Made-with: Cursor
* allow SFT in instructions, RL as ultimate goal
Made-with: Cursor
* add workspace isolation rules to instructions.md
Use relative paths, forbid cd outside workspace, ignore symlink targets.
Made-with: Cursor
* update opencode start.sh: use OPENCODE_PYTHON, add PATH for opencode CLI, remove unsupported args
Made-with: Cursor
* opencode start.sh: pass --run-dir to use AutoRL-Bench workspace
Ensures OpenCode-FSM-Runner writes outputs into the workspace prepared
by AutoRL-Bench instead of creating its own runs/ directory.
Made-with: Cursor
* opencode start.sh: prepend training env bin to PATH
Ensures LLM agent bash calls (e.g. python3 -c "from trl import ...")
resolve to the correct training environment, instead of relying on
parent shell conda activation.
Made-with: Cursor
* opencode start.sh: restore --max-retries and --eval-timeout for opencode-rl
Made-with: Cursor
* add humaneval benchmark
* Replace import * cleanup hack with explicit imports in OpenCompass config
- Resolve dataset variable names via importlib before generating config,
so the template uses `from xxx import datasets` instead of `import *`
- Remove the fragile runtime cleanup hack that set leaked modules to None
- Increase OpenCompass timeout from 3600s to 7200s
- Fix score parsing to average across multiple subdatasets
* refine opencompass config file generating
* add humaneval benchmark dependency instructions
human-eval package requires clone from open-compass/human-eval with
a one-line patch to relax assertion for partial evaluation (test split only).
Made-with: Cursor
* fix: sanitize user-provided paths in RL UI (CodeQL)
* fix: resolve user path relative to safe root (CodeQL)
* fix: use Copilot-suggested path sanitization pattern (CodeQL)
* fix: normalize and reject absolute user paths (CodeQL)
* Fix training params, vLLM OOM cleanup, OpenCompass score parsing, and baseline cache logic
* fix: add setuptools<75 to requirements for opencompass pkg_resources dependency
uv venv does not include setuptools by default, causing OpenCompass baseline
evaluation to fail with "No module named 'pkg_resources'".
Made-with: Cursor
* webshop
* feat(autorl_bench): improve smith benchmark integration and evaluator robustness
- Add smith benchmark docs to README: usage examples, discovery mechanism, SMITH_BENCH_DIR
- Improve PerSampleEvaluator: vLLM GPU cleanup, test_range slicing
- Refactor server.py: extract grading server from utils
- Fix OpenCompass score parsing and baseline cache logic
* fix: add smart fallback for OpenCompass dataset variable resolution
When build_dataset_imports_explicit() fails to import an OpenCompass
dataset module (common in grading server subprocess), it now guesses
the correct variable name from the module path convention instead of
falling back to empty names (which causes import * and breaks BBH
due to leaked file handle objects).
* revert: restore opencompass.py to pre-modification state
Revert vLLM pid cleanup, dash-value checks, and metric-based
score parsing added in 31caff2f and bb32e555.
* keep metric-aware score parsing in opencompass; add baseline column to UI
- opencompass.py: retain metric-type filtering (accuracy/score) instead
of naive averaging, avoids polluting scores with pass/timeout counters
- ui.py: add Baseline column to Agent Summary table
Made-with: Cursor
* fix: handle non-string answers in extract_answer to prevent TypeError
arc_agi and other benchmarks can have non-string answer fields (e.g. lists),
which caused a crash in re.search(). Adding str() coercion fixes this.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* update deepsearch qa tasks
* fix: benchmark evaluation reliability (B1-B4)
- B1: auto-detect LoRA adapters and enable vLLM LoRA mode (read base_model from adapter_config.json)
- B2: serialize evaluations with threading.Lock to prevent GPU contention
- B3: cache eval results by model_path to deduplicate concurrent submissions
- B4: propagate error details from OpenCompass to agent (non-numeric scores, load failures)
Made-with: Cursor
* fix(B1): reject LoRA adapter submissions with clear merge instructions
- opencompass.py: detect adapter_config.json and return error with
merge_and_unload() instructions instead of broken vLLM LoRA mode
- instructions.md: add requirement to submit full merged models
- opencompass_template.yaml: remove unused is_lora/lora_path params
Made-with: Cursor
* update chat completion
* update
* update deepsearch
* md
* codex + benchmark update
* codex + benchmark update
* codex + benchmark update
* codex
* codex
* fix: grading server cache key includes mtime to detect model overwrites
Previously cache used only resolved_path, so overwritten models at the
same path returned stale scores. Now cache key = path@max_mtime so
re-evaluation is triggered when model files change.
Made-with: Cursor
* feat: add gemini/claude agent scaffolds, fix codex binary path
- codex/start.sh: use CODEX_BIN env var instead of bare 'codex'
- Add gemini/ and claude/ agent directories with config.yaml and start.sh
Made-with: Cursor
* chore: remove copied human_readable_trace.py from PostTrainBench
Made-with: Cursor
* update evaluation
* benchmark
* Fix log cleanup and OpenHands env
* fix: webshop env pth problem
* benchmark alpacaeval
* style(rl): apply auto-lint fixes
* fix(rl): address CI and CodeQL issues
* fix(rl): make autorl bench imports CI-safe
---------
Co-authored-by: Qizheng Li <jenssenlee@163.com>
Co-authored-by: Young <afe.young@gmail.com>
Co-authored-by: Bowen Xian <xianbowen@outlook.com>
Co-authored-by: chelsea97 <zhuowbrown@gmail.com>
Co-authored-by: Xu Yang <peteryang@vip.qq.com>
Co-authored-by: sakura657 <yctangcse@gmail.com>
Co-authored-by: shatianming5 <tianming.sha@stonybrook.edu>
Co-authored-by: Yeyuqing0913 <shatianming4@gmail.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* refine prompt
* small update
* fix a small bug
* remove debug config after execution
* fix: only remove <think> at start
* feat: support creating dataset & multi-eval frame (#1302)
* feat: add iterative evolve and evaluation support with partial chain stop
* feat: add FTDataEvaluator and support multiple implement functions in finetune
* feat: data implement for pre-proposal and proposal and add datasets (#1303)
* feat:(1) support for multi layer dataset extraction (2) add category.json for dataset in datasets/
* fix: fix bug for generate category.json
* feat: add get_dataset_folder_desc
* init data proposal and merge qzli/ft
* update data proposal prompts and add max_position_embeddings and resolve confilcts
* remove sample counts in data proposal
* turn data and train to unified hypo_gen
* refine prompts
* remove category.json and add it to dataset_info
* fix jinja problem and proposal done
* lint
* add ai-generated description and raw readme into dataset_info.json
* update prompt for description
* add datasets
* initial fix for proposal of data
* final version for data proposal
* lint
* feat: add stats in dataset_info, and enable data coder (#1306)
* refactor(dataset): add stats into dataset_info.json, and remove dataset from gitignore_folder
* feat: enable data coder and run data process
* feat: Merge data coder (#1307)
* feat: implement finetune data coding, evaluation, and config improvements
* fix: deepspeed config path
* fix: dataset info columns
---------
Co-authored-by: Young <afe.young@gmail.com>
* replace str length with token_limit
* add readme to dataset_info and remove useless blank lines in scenario description
* feat: dataset prepare
* fix: extract prams script name
* feat: add loss&predictions samples to feedback
* remove duplicate envs and and add llm_api_preferences and enhance reasoning token limits
* feat: network for ft_env
* fix: remove gpt-4o, which has low quota
* feat: a simple ui
* feat: merge data and train task type (#1309)
* feat: filter redundant prams of lf
* fix: ui bug caused by removing task_type
* fix: force agent to use high concurrency, and remove redundant prompt
* feat: extract info from llama factory log, and check data exists before download
* fix: add compatibility rules
* feat: llm evaluator for data coder
* feat: openai package in ft docker, and refine prompt
* feat: refine ft ui, add more info
* feat: add raw logs
* refine data coder prompt(for feedback debug)
* feat: select dataset in scen init
* fix: ui for docker log seperately
* feat: sync log through blob
* improve ui, and add llm feedback in Runner&Exp2FB (#1312)
* fix: ui bug to visualize docker log, and lint
* feat: unified docker log for ft env, and some refactor
* fix bugs and improve ui
* feat: save log of evaluator(single feedback)
* feat: add evaluator, set cleanup docker log
* feat: call llm in RunnerEvaluator and Feedback
* fix: extract structured error message in RunnerEvaluator
* feat: feedback improve, and fix some bugs
* feat: feedback improve when runner fails
* small update
* feat(UI): add running info and benchmark metric in loop expander
* feat(UI): add render markdown toggle
* feat: refine prompts and add error type in exp2fb
* feat: add filterd params reason, set default benchmark timeout to infinite, and refine train loss express
* recover dataset deepscaler
* feat: set timeout in .env
* refactor: unifiied ft_env timeout
* feat: debug mode for data coder
* feat: deliver data_stats after generate debug_data
* feat: use gpt-5.1 as judge model, set judge_retry, and refine debug mode prompt
* refine prompt
* refactor: llama factory manager logic, and refine data processing prompt
* feat(DockerEnv): support GPU selection via CUDA_VISIBLE_DEVICES
* feat: set api concurrency via .env
* fix: ft env timeout bug
* feat: enable CondaEnv run
* fix: can't update bin path in first run, and path bug in lf manager
* feat(ui): set log path through .env
* refactor(ui): wrap_lines, remove css
* feat(coder): retry when parse code-block fail
* fix: refine single-fb in ui, and fix path bug(not allow proposal to decide path)
* fix: opencompass CondaEnv torch compatible with vllm
* fix: refine error text in coding
* feat: deepspeed config for CondaEnv
* feat: memory estimator
* fix: deepspeed package for condaenv
* fix: use `client.chat.completions.create()` only
* feat: flash attention for condaenv
* feat: strong and weak models interface
* fix: condaenv package dependency
* use multi round conversation in llm finetune proposal
* refine prompt for data processing
* enable evolving in data coder
* maximize output token size
* fix: refine ui
* fix: optional packages for llama factory
* fix: torch denpendency for b200
* fix: opencompass dependency
* update cot prompts
* skip the sub implement
* skip conda preparation if env exists
* update chemcot datasets
* fix: unify docker to use litellm
* update readme and instructions
* fix: set CUDA_VISIBLE_DEVICES for CondaEnv
* feat: add panorama dataset, refactor dataset interface
* feat: calculate token using tiktoken, and ndarray bug
* fix: download subtasks of chemcotdataset seperately
* feat: customized prepare func for datasets
* feat: update new benchmarks
* add datasets package
* docs: readme for llm finetune
* feat: download raw data directly, with post-process function
* feat: analyze raw dataset
* suppress litellm debug info
* feat(ui): summary page
* feat: run multi-jobs
* feat: improve ui
* feat: add path and checkout options to LLM finetune loop entrypoint
* feat: add FinanceIQ_ppl benchmark with auto-download and dataset desc rendering
* refactor: remove unused imports and dead code, fix session folder logging
* feat: enable tablebench and tableInstruct dataset
* refine dataset readme, and coder prompt
* refine proposal and coder prompt
* fix: ui path (default log path)
* feat: add automatic LoRA model merging for benchmarking with vLLM
* refactor: reorganize finetune benchmark and merge modules under benchmark dir
* refactor: modularize benchmark config and error extraction for finetune scenario
* fix: update benchmark import paths and disable env cache for device info
* refactor docke&conda env and fix import bugs
* modify init python file
* feat: add FinanceIQ dataset split utility and integrate with pipeline
* feat: set weak and strong model by env, distribute workload across models
* feat: sample dataset and rm params for tensorboard, wandb
* update script to run jobs
* refine proposal prompt, remove specific dataset name
* fix(ui): auto switch log folder
* fix: estimate the processed full data after sample
* feat: filter raw data more aggressively, and lower data_eval standard
* feat: sync workspace to blob
* feat: rdkit for chemcotbench
* update qwen2.5&llama3.1 context
* fix: force failure on validation error and remove try/except in validator
* feat: unified error sample extraction (with test scripts)
* feat: set conda cache with .env
* feat: skip data eval if data pass in last evo
* fix: rm redundant param
* fix ui bug
* refactor: centralize assign_code_list_to_evo in MultiProcessEvolvingStrategy
* feat: add test_params.yaml generation and workspace cleanup improvements for finetune
* refactor: replace get_clear_ws_cmd with clear_workspace and update prompts for hard check criteria
* add bioprobench dataset
* fix: handle commas in training config extraction and refactor prompt includes
* bioprobench description
* add bioprobench readme
* feat: merge lora adapter for blackwell gpu
* feat: support for multi benchmarks in one job
* change dfficult aware content for training
* update difficulty-aware and logging principles
* fix: resolve variable name conflict in FTRunnerEvaluator
* set job id accuracy to minute
* feat(ui): display one selected metric per benchmark
* feat: store sota exp, and fix ws_ckp bug
* fix: truncate data.json in feedback
* fix: opencompass data for conda env
* fix: save only the last model
* feat: set log path and ws path
* fix: set overwrite_cache to avoid lock contention(through injecting params)
* feat: redirect stdout to file in localenv
* add pickle cache to dataset desc
* fix CI
* fix: remove redundant wrapper
* feat: set python_unbuffered
* move redirect stdout to env run
* fix a small bug
* move model folder
* feat(ui): display benchmark baseline
* fix: enrich scenario and benchmark description
* fix: rewrite runner eval to accept easier
* feat: compare with baseline when no SOTA
* update tablebench readme
* fix: switch back to single benchmark (for baseline)
* feat(ui): add ws path in ui
* refactor: update SOTA tracking to use DAG traversal and parent selection
* fix: prioritize local_selection in trace and refactor sibling retrieval logic
* refactor: unify error handling in feedback generation and update workspace injection
* feat: add skip_loop_error_stepname to control error skip step in LoopBase
* fix: set local_selection to NEW_ROOT for experiments without parent
* feat: set different ports for jobs
* feat: set different ports for jobs
* feat: add upper data size limit for LLM fine-tuning and update related prompts
* fix: replace get_truncated_stdout() with stdout for consistent output handling
* refactor: remove data.json from cache and workspace logic, focus on script-based reuse
* fix: rm target_scenario
* feat: add selective cache extraction and custom cache key for data processing
* fix(ui): bug when displaying tablebench
* fix: filter config in dataset_info.json
* feat: add test set, set valid set
* feat(ui): update test score, and set color for final decision
* feat: add test score for baseline and update ui
* fix: use [-100:] as test range
* feat: update data_stats in runner
* feat: wait for opencompass init when run multi jobs
* fix: adjust test&valid split
* feat: force to generate COT(with <think> token), and add answer format in scenarios.json
* feat: improve ui
* fix: unify benchmark volume mounts and set extra_volumes for conda env
* fix(ui): number color
* fix: update GPU memory handling to use total memory in GB and streamline code
* fix: set use_cot_postprocessor
* feat: add env_dict to config classes and merge env vars in Env run
* fix: let coder obey proposal
* fix(ui): direction bug and update chemcot core metirc
* fix: set consistent benchmark mount points and env vars for docker and conda
* fix: addintional target for LoRA
* feat: workspace dir log for benchmark running
* fix: tableInstruct path bug and update benchmark description
* feat: timeout for whole job
* fix: align FinanceIQ import to opencompass
* feat: use llm_judge for FinanceIQ
* feat: switch to turn on <think> or not
* feat: using scripts to redirect stdout, and run in different windows
* feat: sync litellm log
* fix: gpu memory format
* fix: escape special characters in benchmark desc
* fix: set data processing timeout to 1h
* feat: set valid_loss and save_best_model
* fix: inject timeout and stage
* fix: loss history extract logic
* feat: inject output dir
* feat: inject eval batch size
* feat: inject save_total_limit
* feat: update data prompt
* fix: escape shell special characters
* fix: tablebench visualization UI
* fix: move implementation validation to coder, and ignore injected params
* feat: README for FinanceIQ dataset
* fix: bioprobench desc error
* fix: remove task alignment when coder eval
* fix: FinanceIQ now extracts last capital as answer
* fix: stdout contains binary data
* feat: recover estimate full output and set eval setting automatically
* fix(ui): precision for summary table
* fix(ui): import error
* feat: try to use lora
* fix(api): fix litellm bug for code block
* fix: refine prompts to give agent more decision space
* chore(ci): fix mypy typing issues
* chore(ci): format code with black
* chore(ci): fix ruff lint violations
* chore(ci): sort imports with isort
* chore(ci): format code with black
* test: temporarily skip extract_parameters imports due to numpy pin
* fix: compatibility issues for qlib scenarios on finetune branch
* fix(fin_factor): skip to fb for coder error
* fix(loop): default skip to feedback step on skip_loop_error
When skip_loop_error exception happens and skip_loop_error_stepname is not
explicitly set, default to jumping to 'feedback' step if it exists,
otherwise fall back to the last step (record).
This prevents KeyError when record step tries to access feedback data that
doesn't exist because we skipped the feedback phase.
Also removed redundant skip_loop_error_stepname from finetune loop since
it's now the default behavior.
* add 'skip to record' to DS scenario like other scenarios
* fix 2 scenarios bug about rd_loop class
* fix: lint(mypy, ruff, black) error
* fix: mypy lint error
* fix data science scenario bug
---------
Co-authored-by: Xu Yang <peteryang@vip.qq.com>
Co-authored-by: Qizheng Li <jenssenlee@163.com>
Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
Co-authored-by: amstrongzyf <201840057@smail.nju.edu.cn>
Co-authored-by: Young <afe.young@gmail.com>
Co-authored-by: amstrongzyf <amstrongzyf@126.com>
Co-authored-by: chelsea97 <zhuowbrown@gmail.com>
Co-authored-by: SunsetWolf <Lv.Linlang@hotmail.com>
* fix: prevent calendar index overflow when signal data ends early
* fix: make test_end optional to resolve Qlib backtest calendar misalignment
* fix: enhance GPU information output in get_gpu_info function
* fix: improve GPU information output in get_gpu_info function for better clarity
---------
Co-authored-by: Xu Yang <peteryang@vip.qq.com>
* refactor: unify qlib experiment configs, runners, and templates
* fix: use PropSetting instances instead of class attributes in qlib runners
* docs: add configurable train/valid/test time segments for fintech scenarios
* fix: refine task scheduling logic in MultiProcessEvolvingStrategy for improved handling of feedback in improve mode
* fix: add empty implementation for skipped tasks in MultiProcessEvolvingStrategy
* add simple rag mcp
* add rag_agent in expGen v2
* add conf config for research rag
* fix CI
* refactor: move context7 and rag config files to new conf modules
* make rag agent general
* fix CI
---------
Co-authored-by: Young <afe.young@gmail.com>
* feat: add interactor classes and user interaction handling for experiments
* update code
* use fragment retry mechanism instead of rerun()
* fix a bug
* integrate user instructions into proposal and coder
* fix CI
* fix CI
* feat: add approval option for user instructions submission
* feat: enhance user instructions handling in Task and DSExperiment classes
* fix CI
* add user instructions into hypothesis rewrite
* add interface to command line
---------
Co-authored-by: Bowen Xian <xianbowen@outlook.com>
* chore: add medal info
* return sota_exp_stat
* update hit check
* update experiment
* udpate experiment
* extract log
* remove old log folder
* update candidates
* keep highest score
* early stop if no medal candidate
TPTBusiness