Releasesby IbisUnparseable trajectories are discarded
Update2 days agoUnverifiedAdded Oct 22, 2026
A parse failure makes the whole trajectory untrainable, including earlier successful turns. The cookbook RL loop drops it without retrying or stopping the run, and other rollouts continue. Parse failures don't count toward the infrastructure circuit breaker, which now classifies transient failures more accurately and reports failure counts by reason.
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Every Fireworks AI platform releaseErrors identify their source
Before 1.2.27, the sidecar returned HTTP 502 for two failures that weren't server-side: tito_model_malformed_output (unparseable model output) and tito_upstream_error (any sampling exception, including client timeouts and dropped connections). They are replaced by: tito_sdk_parse_error (422): the output couldn't be parsed into an assistant or tool message.
Failure traces are saved locally
Each failed trajectory leaves a private JSONL trace (request, prepared token IDs, raw completion, exception chain) in fireworks-tito-traces under the system temp directory, capped at 256 MiB. Traces from clean trajectories are deleted, and failures rejected by cookbook adapters use the same location and limits.
Image inputs for FiretitanSamplingClient.sample
Since 1.2.27, sample and sample_async take a keyword-only images= list of base64 data URLs for token prompts with one placeholder per image. Prompts with unprojected image chunks are rejected so sampler-owned delimiters aren't duplicated. See the cookbook's TITO failure debugging reference for every error code and how to read a trace.
Action required
If you use any of the models below on serverless, migrate to a recommended replacement before November 7, 2026 . After that date, they will no longer be available via serverless endpoints.
Recommended migrations
- GPT OSS 120B , move to a dedicated deployment of GPT OSS 120B Inkling , migrate to Muse Spark Open (US endpoint, 50% US regional premium waived) NVIDIA Nemotron 3 Ultra , move to a dedicated deployment of Nemotron Super VL NVIDIA Nemotron 3.5 Lightning 30B A3B , move to a dedicated deployment of Nemotron Super VL MiniMax M3 (US endpoint only) , migrate to...
New models on Serverless
- DeepSeek V4.1 Flash 1017: accounts/fireworks/models/deepseek-v4p1-flash-1017 DeepSeek V4.1 Flash 1017 (US endpoint): accounts/fireworks/models/deepseek-v4p1-flash-1017-us Muse Spark Open (US endpoint, 50% US regional premium waived): accounts/fireworks/models/muse-spark-open MiniMax M3.1: coming to serverless after its open-source release, expected...
Training Upcoming Serverless Training deprecation: DeepSeek V4 Flash (0731) On November 2, 2026 , DeepSeek V4...
Training Upcoming Serverless Training deprecation: DeepSeek V4 Flash (0731) On November 2, 2026 , DeepSeek V4 Flash (0731) will be removed from the shared Serverless Training pool. This change applies only to Serverless Training. If you train DeepSeek V4 Flash (0731) on Serverless Training, migrate to DeepSeek V4.1 Flash 1017 before November 2, 2026.
Training New Serverless Training model: Muse Spark Open Muse Spark Open is now available for LoRA workloads...
Training New Serverless Training model: Muse Spark Open Muse Spark Open is now available for LoRA workloads on the shared Serverless Training pool, with up to 262K context. Use accounts/fireworks/models/muse-spark-open-nvfp4 as the base model. See the Serverless Training guide for setup and the training model catalog for current availability.
Also shipped on Oct 22, 2026
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